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add grounding
Browse files- .gitattributes +1 -0
- app.py +31 -18
- en_core_web_sm-3.6.0/LICENSE +19 -0
- en_core_web_sm-3.6.0/LICENSES_SOURCES +66 -0
- en_core_web_sm-3.6.0/README.md +47 -0
- en_core_web_sm-3.6.0/accuracy.json +330 -0
- en_core_web_sm-3.6.0/attribute_ruler/patterns +0 -0
- en_core_web_sm-3.6.0/config.cfg +268 -0
- en_core_web_sm-3.6.0/lemmatizer/lookups/lookups.bin +3 -0
- en_core_web_sm-3.6.0/meta.json +521 -0
- en_core_web_sm-3.6.0/ner/cfg +13 -0
- en_core_web_sm-3.6.0/ner/model +3 -0
- en_core_web_sm-3.6.0/ner/moves +1 -0
- en_core_web_sm-3.6.0/parser/cfg +13 -0
- en_core_web_sm-3.6.0/parser/model +3 -0
- en_core_web_sm-3.6.0/parser/moves +1 -0
- en_core_web_sm-3.6.0/senter/cfg +3 -0
- en_core_web_sm-3.6.0/senter/model +3 -0
- en_core_web_sm-3.6.0/tagger/cfg +57 -0
- en_core_web_sm-3.6.0/tagger/model +3 -0
- en_core_web_sm-3.6.0/tok2vec/cfg +3 -0
- en_core_web_sm-3.6.0/tok2vec/model +3 -0
- en_core_web_sm-3.6.0/tokenizer +3 -0
- en_core_web_sm-3.6.0/vocab/key2row +1 -0
- en_core_web_sm-3.6.0/vocab/lookups.bin +3 -0
- en_core_web_sm-3.6.0/vocab/strings.json +0 -0
- en_core_web_sm-3.6.0/vocab/vectors +0 -0
- en_core_web_sm-3.6.0/vocab/vectors.cfg +3 -0
- examples/4.png +0 -0
- examples/5.jpg +0 -0
- examples/6.jpg +0 -0
- examples/example_inputs.jsonl +4 -1
- requirements.txt +8 -0
- utils.py +86 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
*model filter=lfs diff=lfs merge=lfs -text
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app.py
CHANGED
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@@ -9,7 +9,10 @@ import time
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DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVLM">VisualGLM</a>'''
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MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry
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NOTES = 'This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model.'
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@@ -17,6 +20,7 @@ import json
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import requests
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import base64
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import hashlib
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default_chatbox = [("", "Hi, What do you want to know about this image?")]
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@@ -45,6 +49,7 @@ def process_image_without_resize(image_prompt):
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timestamp = int(time.time())
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file_ext = os.path.splitext(image_prompt)[1]
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filename = f"examples/{timestamp}{file_ext}"
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image.save(filename)
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print(f"temporal filename {filename}")
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with open(filename, "rb") as image_file:
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encoded_img = str(bytes, encoding='utf-8')
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image_hash = hashlib.sha256(bytes).hexdigest()
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os.remove(filename)
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return encoded_img, image_hash
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def is_chinese(text):
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top_p,
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image_prompt,
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result_previous,
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hidden_image
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):
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result_text = [(ele[0], ele[1]) for ele in result_previous]
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for i in range(len(result_text)-1, -1, -1):
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if result_text[i][0] == "":
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del result_text[i]
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print(f"history {result_text}")
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"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
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}
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if image_prompt:
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encoded_img, image_hash = process_image_without_resize(image_prompt)
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print(f"image_hash:{image_hash}, hidden_image_hash:{hidden_image}")
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if hidden_image is not None and image_hash != hidden_image:
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else:
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encoded_img = None
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print('
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data = json.dumps({
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'text': input_text,
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'image': encoded_img,
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'temperature': temperature,
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'top_p': top_p,
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'history': result_text
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})
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try:
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response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json()
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else:
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result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
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return "", result_text, hidden_image
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-
print('
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# response = {'result':input_text}
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answer = str(response['result'])
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-
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print(result_text)
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print('finished')
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return "", result_text, hidden_image
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clear_button = gr.Button('Clear')
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image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
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with gr.Row():
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temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
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top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
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with gr.Group():
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with gr.Row():
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with gr.Column(scale=7):
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maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1)
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with gr.Column(scale=2):
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change_button = gr.Button('Change hint to English', visible=False)
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with gr.Column(scale=5.5):
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result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
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hidden_image_hash = gr.Textbox(visible=False)
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@@ -173,14 +185,15 @@ def main():
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gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples],
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inputs=[input_text, image_prompt],
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label="Example Inputs (Click to insert an examplet into the input box)",
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examples_per_page=
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gr.Markdown(NOTES)
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print(gr.__version__)
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run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash],
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outputs=[input_text, result_text, hidden_image_hash])
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input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash],
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outputs=[input_text, result_text, hidden_image_hash])
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clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
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image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
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DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVLM">VisualGLM</a>'''
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MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.<br>Hint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.'
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GROUNDING_NOTICE = 'Hint: When you check "Grounding", please use the <a href="https://github.com/THUDM/CogVLM/blob/main/utils/template.py#L344">corresponding prompt</a> or the examples below.'
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NOTES = 'This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model.'
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import requests
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import base64
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import hashlib
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from utils import parse_response
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default_chatbox = [("", "Hi, What do you want to know about this image?")]
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timestamp = int(time.time())
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file_ext = os.path.splitext(image_prompt)[1]
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filename = f"examples/{timestamp}{file_ext}"
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+
filename_grounding = f"examples/{timestamp}_grounding{file_ext}"
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image.save(filename)
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print(f"temporal filename {filename}")
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with open(filename, "rb") as image_file:
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encoded_img = str(bytes, encoding='utf-8')
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image_hash = hashlib.sha256(bytes).hexdigest()
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os.remove(filename)
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return image, encoded_img, image_hash, filename_grounding
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def is_chinese(text):
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top_p,
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image_prompt,
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result_previous,
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hidden_image,
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grounding
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):
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result_text = [(ele[0], ele[1]) for ele in result_previous]
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for i in range(len(result_text)-1, -1, -1):
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if result_text[i][0] == "" or result_text[i][0] == None:
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del result_text[i]
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print(f"history {result_text}")
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"User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
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}
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if image_prompt:
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+
pil_img, encoded_img, image_hash, image_path_grounding = process_image_without_resize(image_prompt)
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print(f"image_hash:{image_hash}, hidden_image_hash:{hidden_image}")
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if hidden_image is not None and image_hash != hidden_image:
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else:
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encoded_img = None
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print('request chat model...' if not grounding else 'request grounding model...')
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data = json.dumps({
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'text': input_text,
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'image': encoded_img,
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'temperature': temperature,
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'top_p': top_p,
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'history': result_text,
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'is_grounding': grounding
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})
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try:
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response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json()
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else:
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result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
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return "", result_text, hidden_image
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+
print('request done...')
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# response = {'result':input_text}
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answer = str(response['result'])
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+
if grounding:
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parse_response(pil_img, answer, image_path_grounding)
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new_answer = answer.replace(input_text, "")
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result_text.append((input_text, new_answer))
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result_text.append((None, (image_path_grounding,)))
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else:
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result_text.append((input_text, answer))
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print(result_text)
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print('finished')
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return "", result_text, hidden_image
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clear_button = gr.Button('Clear')
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image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
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with gr.Row():
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grounding = gr.Checkbox(label="Grounding")
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with gr.Row():
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grounding_notice = gr.Markdown(GROUNDING_NOTICE)
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+
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with gr.Row():
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temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
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top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
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with gr.Column(scale=5.5):
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| 182 |
result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
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hidden_image_hash = gr.Textbox(visible=False)
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gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples],
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| 186 |
inputs=[input_text, image_prompt],
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| 187 |
label="Example Inputs (Click to insert an examplet into the input box)",
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+
examples_per_page=6)
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gr.Markdown(MAINTENANCE_NOTICE1)
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gr.Markdown(NOTES)
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print(gr.__version__)
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+
run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
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| 195 |
outputs=[input_text, result_text, hidden_image_hash])
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+
input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
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| 197 |
outputs=[input_text, result_text, hidden_image_hash])
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| 198 |
clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
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| 199 |
image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
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en_core_web_sm-3.6.0/LICENSE
ADDED
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+
Copyright 2021 ExplosionAI GmbH
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| 2 |
+
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| 3 |
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Permission is hereby granted, free of charge, to any person obtaining a copy of
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| 4 |
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this software and associated documentation files (the "Software"), to deal in
|
| 5 |
+
the Software without restriction, including without limitation the rights to
|
| 6 |
+
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
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| 7 |
+
of the Software, and to permit persons to whom the Software is furnished to do
|
| 8 |
+
so, subject to the following conditions:
|
| 9 |
+
|
| 10 |
+
The above copyright notice and this permission notice shall be included in all
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| 11 |
+
copies or substantial portions of the Software.
|
| 12 |
+
|
| 13 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 14 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 15 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 16 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 17 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 18 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 19 |
+
SOFTWARE.
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en_core_web_sm-3.6.0/LICENSES_SOURCES
ADDED
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| 1 |
+
# OntoNotes 5
|
| 2 |
+
|
| 3 |
+
* Author: Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston
|
| 4 |
+
* URL: https://catalog.ldc.upenn.edu/LDC2013T19
|
| 5 |
+
* License: commercial (licensed by Explosion)
|
| 6 |
+
|
| 7 |
+
```
|
| 8 |
+
```
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# ClearNLP Constituent-to-Dependency Conversion
|
| 14 |
+
|
| 15 |
+
* Author: Emory University
|
| 16 |
+
* URL: https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md
|
| 17 |
+
* License: Citation provided for reference, no code packaged with model
|
| 18 |
+
|
| 19 |
+
```
|
| 20 |
+
```
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# WordNet 3.0
|
| 26 |
+
|
| 27 |
+
* Author: Princeton University
|
| 28 |
+
* URL: https://wordnet.princeton.edu/
|
| 29 |
+
* License: WordNet 3.0 License
|
| 30 |
+
|
| 31 |
+
```
|
| 32 |
+
WordNet Release 3.0
|
| 33 |
+
|
| 34 |
+
This software and database is being provided to you, the LICENSEE, by
|
| 35 |
+
Princeton University under the following license. By obtaining, using
|
| 36 |
+
and/or copying this software and database, you agree that you have
|
| 37 |
+
read, understood, and will comply with these terms and conditions.:
|
| 38 |
+
|
| 39 |
+
Permission to use, copy, modify and distribute this software and
|
| 40 |
+
database and its documentation for any purpose and without fee or
|
| 41 |
+
royalty is hereby granted, provided that you agree to comply with
|
| 42 |
+
the following copyright notice and statements, including the disclaimer,
|
| 43 |
+
and that the same appear on ALL copies of the software, database and
|
| 44 |
+
documentation, including modifications that you make for internal
|
| 45 |
+
use or for distribution.
|
| 46 |
+
|
| 47 |
+
WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved.
|
| 48 |
+
|
| 49 |
+
THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON
|
| 50 |
+
UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
|
| 51 |
+
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON
|
| 52 |
+
UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT-
|
| 53 |
+
ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE
|
| 54 |
+
OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT
|
| 55 |
+
INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR
|
| 56 |
+
OTHER RIGHTS.
|
| 57 |
+
|
| 58 |
+
The name of Princeton University or Princeton may not be used in
|
| 59 |
+
advertising or publicity pertaining to distribution of the software
|
| 60 |
+
and/or database. Title to copyright in this software, database and
|
| 61 |
+
any associated documentation shall at all times remain with
|
| 62 |
+
Princeton University and LICENSEE agrees to preserve same.```
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
|
en_core_web_sm-3.6.0/README.md
ADDED
|
@@ -0,0 +1,47 @@
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|
| 1 |
+
### Details: https://spacy.io/models/en#en_core_web_sm
|
| 2 |
+
|
| 3 |
+
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
|
| 4 |
+
|
| 5 |
+
| Feature | Description |
|
| 6 |
+
| --- | --- |
|
| 7 |
+
| **Name** | `en_core_web_sm` |
|
| 8 |
+
| **Version** | `3.6.0` |
|
| 9 |
+
| **spaCy** | `>=3.6.0,<3.7.0` |
|
| 10 |
+
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
|
| 11 |
+
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
|
| 12 |
+
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
|
| 13 |
+
| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
|
| 14 |
+
| **License** | `MIT` |
|
| 15 |
+
| **Author** | [Explosion](https://explosion.ai) |
|
| 16 |
+
|
| 17 |
+
### Label Scheme
|
| 18 |
+
|
| 19 |
+
<details>
|
| 20 |
+
|
| 21 |
+
<summary>View label scheme (113 labels for 3 components)</summary>
|
| 22 |
+
|
| 23 |
+
| Component | Labels |
|
| 24 |
+
| --- | --- |
|
| 25 |
+
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
|
| 26 |
+
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
|
| 27 |
+
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
|
| 28 |
+
|
| 29 |
+
</details>
|
| 30 |
+
|
| 31 |
+
### Accuracy
|
| 32 |
+
|
| 33 |
+
| Type | Score |
|
| 34 |
+
| --- | --- |
|
| 35 |
+
| `TOKEN_ACC` | 99.86 |
|
| 36 |
+
| `TOKEN_P` | 99.57 |
|
| 37 |
+
| `TOKEN_R` | 99.58 |
|
| 38 |
+
| `TOKEN_F` | 99.57 |
|
| 39 |
+
| `TAG_ACC` | 97.25 |
|
| 40 |
+
| `SENTS_P` | 92.02 |
|
| 41 |
+
| `SENTS_R` | 89.21 |
|
| 42 |
+
| `SENTS_F` | 90.59 |
|
| 43 |
+
| `DEP_UAS` | 91.75 |
|
| 44 |
+
| `DEP_LAS` | 89.87 |
|
| 45 |
+
| `ENTS_P` | 84.55 |
|
| 46 |
+
| `ENTS_R` | 84.57 |
|
| 47 |
+
| `ENTS_F` | 84.56 |
|
en_core_web_sm-3.6.0/accuracy.json
ADDED
|
@@ -0,0 +1,330 @@
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|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"token_acc": 0.9986194413,
|
| 3 |
+
"token_p": 0.9956819193,
|
| 4 |
+
"token_r": 0.9957659295,
|
| 5 |
+
"token_f": 0.9957239226,
|
| 6 |
+
"tag_acc": 0.97246532,
|
| 7 |
+
"sents_p": 0.9201877934,
|
| 8 |
+
"sents_r": 0.8921432812,
|
| 9 |
+
"sents_f": 0.9059485531,
|
| 10 |
+
"dep_uas": 0.9175304332,
|
| 11 |
+
"dep_las": 0.89874821,
|
| 12 |
+
"dep_las_per_type": {
|
| 13 |
+
"prep": {
|
| 14 |
+
"p": 0.853521338,
|
| 15 |
+
"r": 0.8635932461,
|
| 16 |
+
"f": 0.8585277532
|
| 17 |
+
},
|
| 18 |
+
"det": {
|
| 19 |
+
"p": 0.9763930156,
|
| 20 |
+
"r": 0.9781048683,
|
| 21 |
+
"f": 0.9772481923
|
| 22 |
+
},
|
| 23 |
+
"pobj": {
|
| 24 |
+
"p": 0.9613764045,
|
| 25 |
+
"r": 0.967681131,
|
| 26 |
+
"f": 0.9645184649
|
| 27 |
+
},
|
| 28 |
+
"nsubj": {
|
| 29 |
+
"p": 0.9565737052,
|
| 30 |
+
"r": 0.9467250821,
|
| 31 |
+
"f": 0.9516239128
|
| 32 |
+
},
|
| 33 |
+
"aux": {
|
| 34 |
+
"p": 0.9815061794,
|
| 35 |
+
"r": 0.9827294578,
|
| 36 |
+
"f": 0.9821174377
|
| 37 |
+
},
|
| 38 |
+
"advmod": {
|
| 39 |
+
"p": 0.8548033091,
|
| 40 |
+
"r": 0.8519266364,
|
| 41 |
+
"f": 0.8533625485
|
| 42 |
+
},
|
| 43 |
+
"relcl": {
|
| 44 |
+
"p": 0.7571736011,
|
| 45 |
+
"r": 0.7659651669,
|
| 46 |
+
"f": 0.7615440115
|
| 47 |
+
},
|
| 48 |
+
"root": {
|
| 49 |
+
"p": 0.9195942266,
|
| 50 |
+
"r": 0.8910218352,
|
| 51 |
+
"f": 0.9050825879
|
| 52 |
+
},
|
| 53 |
+
"xcomp": {
|
| 54 |
+
"p": 0.8836222144,
|
| 55 |
+
"r": 0.8966259871,
|
| 56 |
+
"f": 0.8900766079
|
| 57 |
+
},
|
| 58 |
+
"amod": {
|
| 59 |
+
"p": 0.9174389766,
|
| 60 |
+
"r": 0.9107223842,
|
| 61 |
+
"f": 0.9140683422
|
| 62 |
+
},
|
| 63 |
+
"compound": {
|
| 64 |
+
"p": 0.9126489559,
|
| 65 |
+
"r": 0.9298284696,
|
| 66 |
+
"f": 0.9211586207
|
| 67 |
+
},
|
| 68 |
+
"poss": {
|
| 69 |
+
"p": 0.9739583333,
|
| 70 |
+
"r": 0.9786634461,
|
| 71 |
+
"f": 0.9763052209
|
| 72 |
+
},
|
| 73 |
+
"ccomp": {
|
| 74 |
+
"p": 0.7671207315,
|
| 75 |
+
"r": 0.8372708758,
|
| 76 |
+
"f": 0.8006621872
|
| 77 |
+
},
|
| 78 |
+
"attr": {
|
| 79 |
+
"p": 0.899837794,
|
| 80 |
+
"r": 0.93313709,
|
| 81 |
+
"f": 0.9161849711
|
| 82 |
+
},
|
| 83 |
+
"case": {
|
| 84 |
+
"p": 0.9787549407,
|
| 85 |
+
"r": 0.9914914915,
|
| 86 |
+
"f": 0.9850820487
|
| 87 |
+
},
|
| 88 |
+
"mark": {
|
| 89 |
+
"p": 0.9068783069,
|
| 90 |
+
"r": 0.9083200848,
|
| 91 |
+
"f": 0.9075986232
|
| 92 |
+
},
|
| 93 |
+
"intj": {
|
| 94 |
+
"p": 0.6717131474,
|
| 95 |
+
"r": 0.6175824176,
|
| 96 |
+
"f": 0.6435114504
|
| 97 |
+
},
|
| 98 |
+
"advcl": {
|
| 99 |
+
"p": 0.6633986928,
|
| 100 |
+
"r": 0.6645681189,
|
| 101 |
+
"f": 0.6639828909
|
| 102 |
+
},
|
| 103 |
+
"cc": {
|
| 104 |
+
"p": 0.8323511726,
|
| 105 |
+
"r": 0.8277717976,
|
| 106 |
+
"f": 0.8300551691
|
| 107 |
+
},
|
| 108 |
+
"neg": {
|
| 109 |
+
"p": 0.9466865969,
|
| 110 |
+
"r": 0.9533366784,
|
| 111 |
+
"f": 0.95
|
| 112 |
+
},
|
| 113 |
+
"conj": {
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| 114 |
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|
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},
|
| 329 |
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"speed": 8516.5316928954
|
| 330 |
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}
|
en_core_web_sm-3.6.0/attribute_ruler/patterns
ADDED
|
Binary file (14.8 kB). View file
|
|
|
en_core_web_sm-3.6.0/config.cfg
ADDED
|
@@ -0,0 +1,268 @@
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|
|
| 1 |
+
[paths]
|
| 2 |
+
train = null
|
| 3 |
+
dev = null
|
| 4 |
+
vectors = null
|
| 5 |
+
init_tok2vec = null
|
| 6 |
+
|
| 7 |
+
[system]
|
| 8 |
+
gpu_allocator = null
|
| 9 |
+
seed = 0
|
| 10 |
+
|
| 11 |
+
[nlp]
|
| 12 |
+
lang = "en"
|
| 13 |
+
pipeline = ["tok2vec","tagger","parser","senter","attribute_ruler","lemmatizer","ner"]
|
| 14 |
+
disabled = ["senter"]
|
| 15 |
+
before_creation = null
|
| 16 |
+
after_creation = null
|
| 17 |
+
after_pipeline_creation = null
|
| 18 |
+
batch_size = 256
|
| 19 |
+
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
|
| 20 |
+
|
| 21 |
+
[components]
|
| 22 |
+
|
| 23 |
+
[components.attribute_ruler]
|
| 24 |
+
factory = "attribute_ruler"
|
| 25 |
+
scorer = {"@scorers":"spacy.attribute_ruler_scorer.v1"}
|
| 26 |
+
validate = false
|
| 27 |
+
|
| 28 |
+
[components.lemmatizer]
|
| 29 |
+
factory = "lemmatizer"
|
| 30 |
+
mode = "rule"
|
| 31 |
+
model = null
|
| 32 |
+
overwrite = false
|
| 33 |
+
scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
|
| 34 |
+
|
| 35 |
+
[components.ner]
|
| 36 |
+
factory = "ner"
|
| 37 |
+
incorrect_spans_key = null
|
| 38 |
+
moves = null
|
| 39 |
+
scorer = {"@scorers":"spacy.ner_scorer.v1"}
|
| 40 |
+
update_with_oracle_cut_size = 100
|
| 41 |
+
|
| 42 |
+
[components.ner.model]
|
| 43 |
+
@architectures = "spacy.TransitionBasedParser.v2"
|
| 44 |
+
state_type = "ner"
|
| 45 |
+
extra_state_tokens = false
|
| 46 |
+
hidden_width = 64
|
| 47 |
+
maxout_pieces = 2
|
| 48 |
+
use_upper = true
|
| 49 |
+
nO = null
|
| 50 |
+
|
| 51 |
+
[components.ner.model.tok2vec]
|
| 52 |
+
@architectures = "spacy.Tok2Vec.v2"
|
| 53 |
+
|
| 54 |
+
[components.ner.model.tok2vec.embed]
|
| 55 |
+
@architectures = "spacy.MultiHashEmbed.v2"
|
| 56 |
+
width = 96
|
| 57 |
+
attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
|
| 58 |
+
rows = [5000,1000,2500,2500]
|
| 59 |
+
include_static_vectors = false
|
| 60 |
+
|
| 61 |
+
[components.ner.model.tok2vec.encode]
|
| 62 |
+
@architectures = "spacy.MaxoutWindowEncoder.v2"
|
| 63 |
+
width = 96
|
| 64 |
+
depth = 4
|
| 65 |
+
window_size = 1
|
| 66 |
+
maxout_pieces = 3
|
| 67 |
+
|
| 68 |
+
[components.parser]
|
| 69 |
+
factory = "parser"
|
| 70 |
+
learn_tokens = false
|
| 71 |
+
min_action_freq = 30
|
| 72 |
+
moves = null
|
| 73 |
+
scorer = {"@scorers":"spacy.parser_scorer.v1"}
|
| 74 |
+
update_with_oracle_cut_size = 100
|
| 75 |
+
|
| 76 |
+
[components.parser.model]
|
| 77 |
+
@architectures = "spacy.TransitionBasedParser.v2"
|
| 78 |
+
state_type = "parser"
|
| 79 |
+
extra_state_tokens = false
|
| 80 |
+
hidden_width = 64
|
| 81 |
+
maxout_pieces = 2
|
| 82 |
+
use_upper = true
|
| 83 |
+
nO = null
|
| 84 |
+
|
| 85 |
+
[components.parser.model.tok2vec]
|
| 86 |
+
@architectures = "spacy.Tok2VecListener.v1"
|
| 87 |
+
width = ${components.tok2vec.model.encode:width}
|
| 88 |
+
upstream = "tok2vec"
|
| 89 |
+
|
| 90 |
+
[components.senter]
|
| 91 |
+
factory = "senter"
|
| 92 |
+
overwrite = false
|
| 93 |
+
scorer = {"@scorers":"spacy.senter_scorer.v1"}
|
| 94 |
+
|
| 95 |
+
[components.senter.model]
|
| 96 |
+
@architectures = "spacy.Tagger.v2"
|
| 97 |
+
nO = null
|
| 98 |
+
normalize = false
|
| 99 |
+
|
| 100 |
+
[components.senter.model.tok2vec]
|
| 101 |
+
@architectures = "spacy.Tok2Vec.v2"
|
| 102 |
+
|
| 103 |
+
[components.senter.model.tok2vec.embed]
|
| 104 |
+
@architectures = "spacy.MultiHashEmbed.v2"
|
| 105 |
+
width = 16
|
| 106 |
+
attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
|
| 107 |
+
rows = [1000,500,500,500,50]
|
| 108 |
+
include_static_vectors = false
|
| 109 |
+
|
| 110 |
+
[components.senter.model.tok2vec.encode]
|
| 111 |
+
@architectures = "spacy.MaxoutWindowEncoder.v2"
|
| 112 |
+
width = 16
|
| 113 |
+
depth = 2
|
| 114 |
+
window_size = 1
|
| 115 |
+
maxout_pieces = 2
|
| 116 |
+
|
| 117 |
+
[components.tagger]
|
| 118 |
+
factory = "tagger"
|
| 119 |
+
label_smoothing = 0.0
|
| 120 |
+
neg_prefix = "!"
|
| 121 |
+
overwrite = false
|
| 122 |
+
scorer = {"@scorers":"spacy.tagger_scorer.v1"}
|
| 123 |
+
|
| 124 |
+
[components.tagger.model]
|
| 125 |
+
@architectures = "spacy.Tagger.v2"
|
| 126 |
+
nO = null
|
| 127 |
+
normalize = false
|
| 128 |
+
|
| 129 |
+
[components.tagger.model.tok2vec]
|
| 130 |
+
@architectures = "spacy.Tok2VecListener.v1"
|
| 131 |
+
width = ${components.tok2vec.model.encode:width}
|
| 132 |
+
upstream = "tok2vec"
|
| 133 |
+
|
| 134 |
+
[components.tok2vec]
|
| 135 |
+
factory = "tok2vec"
|
| 136 |
+
|
| 137 |
+
[components.tok2vec.model]
|
| 138 |
+
@architectures = "spacy.Tok2Vec.v2"
|
| 139 |
+
|
| 140 |
+
[components.tok2vec.model.embed]
|
| 141 |
+
@architectures = "spacy.MultiHashEmbed.v2"
|
| 142 |
+
width = ${components.tok2vec.model.encode:width}
|
| 143 |
+
attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY","IS_SPACE"]
|
| 144 |
+
rows = [5000,1000,2500,2500,50,50]
|
| 145 |
+
include_static_vectors = false
|
| 146 |
+
|
| 147 |
+
[components.tok2vec.model.encode]
|
| 148 |
+
@architectures = "spacy.MaxoutWindowEncoder.v2"
|
| 149 |
+
width = 96
|
| 150 |
+
depth = 4
|
| 151 |
+
window_size = 1
|
| 152 |
+
maxout_pieces = 3
|
| 153 |
+
|
| 154 |
+
[corpora]
|
| 155 |
+
|
| 156 |
+
[corpora.dev]
|
| 157 |
+
@readers = "spacy.Corpus.v1"
|
| 158 |
+
path = ${paths.dev}
|
| 159 |
+
gold_preproc = false
|
| 160 |
+
max_length = 0
|
| 161 |
+
limit = 0
|
| 162 |
+
augmenter = null
|
| 163 |
+
|
| 164 |
+
[corpora.train]
|
| 165 |
+
@readers = "spacy.Corpus.v1"
|
| 166 |
+
path = ${paths.train}
|
| 167 |
+
gold_preproc = false
|
| 168 |
+
max_length = 0
|
| 169 |
+
limit = 0
|
| 170 |
+
augmenter = null
|
| 171 |
+
|
| 172 |
+
[training]
|
| 173 |
+
train_corpus = "corpora.train"
|
| 174 |
+
dev_corpus = "corpora.dev"
|
| 175 |
+
seed = ${system:seed}
|
| 176 |
+
gpu_allocator = ${system:gpu_allocator}
|
| 177 |
+
dropout = 0.1
|
| 178 |
+
accumulate_gradient = 1
|
| 179 |
+
patience = 5000
|
| 180 |
+
max_epochs = 0
|
| 181 |
+
max_steps = 100000
|
| 182 |
+
eval_frequency = 1000
|
| 183 |
+
frozen_components = []
|
| 184 |
+
before_to_disk = null
|
| 185 |
+
annotating_components = []
|
| 186 |
+
before_update = null
|
| 187 |
+
|
| 188 |
+
[training.batcher]
|
| 189 |
+
@batchers = "spacy.batch_by_words.v1"
|
| 190 |
+
discard_oversize = false
|
| 191 |
+
tolerance = 0.2
|
| 192 |
+
get_length = null
|
| 193 |
+
|
| 194 |
+
[training.batcher.size]
|
| 195 |
+
@schedules = "compounding.v1"
|
| 196 |
+
start = 100
|
| 197 |
+
stop = 1000
|
| 198 |
+
compound = 1.001
|
| 199 |
+
t = 0.0
|
| 200 |
+
|
| 201 |
+
[training.logger]
|
| 202 |
+
@loggers = "spacy.ConsoleLogger.v1"
|
| 203 |
+
progress_bar = false
|
| 204 |
+
|
| 205 |
+
[training.optimizer]
|
| 206 |
+
@optimizers = "Adam.v1"
|
| 207 |
+
beta1 = 0.9
|
| 208 |
+
beta2 = 0.999
|
| 209 |
+
L2_is_weight_decay = true
|
| 210 |
+
L2 = 0.01
|
| 211 |
+
grad_clip = 1.0
|
| 212 |
+
use_averages = true
|
| 213 |
+
eps = 0.00000001
|
| 214 |
+
learn_rate = 0.001
|
| 215 |
+
|
| 216 |
+
[training.score_weights]
|
| 217 |
+
tag_acc = 0.16
|
| 218 |
+
dep_uas = 0.0
|
| 219 |
+
dep_las = 0.16
|
| 220 |
+
dep_las_per_type = null
|
| 221 |
+
sents_p = null
|
| 222 |
+
sents_r = null
|
| 223 |
+
sents_f = 0.02
|
| 224 |
+
lemma_acc = 0.5
|
| 225 |
+
ents_f = 0.16
|
| 226 |
+
ents_p = 0.0
|
| 227 |
+
ents_r = 0.0
|
| 228 |
+
ents_per_type = null
|
| 229 |
+
speed = 0.0
|
| 230 |
+
|
| 231 |
+
[pretraining]
|
| 232 |
+
|
| 233 |
+
[initialize]
|
| 234 |
+
vocab_data = null
|
| 235 |
+
vectors = ${paths.vectors}
|
| 236 |
+
init_tok2vec = ${paths.init_tok2vec}
|
| 237 |
+
before_init = null
|
| 238 |
+
after_init = null
|
| 239 |
+
|
| 240 |
+
[initialize.components]
|
| 241 |
+
|
| 242 |
+
[initialize.components.ner]
|
| 243 |
+
|
| 244 |
+
[initialize.components.ner.labels]
|
| 245 |
+
@readers = "spacy.read_labels.v1"
|
| 246 |
+
path = "corpus/labels/ner.json"
|
| 247 |
+
require = false
|
| 248 |
+
|
| 249 |
+
[initialize.components.parser]
|
| 250 |
+
|
| 251 |
+
[initialize.components.parser.labels]
|
| 252 |
+
@readers = "spacy.read_labels.v1"
|
| 253 |
+
path = "corpus/labels/parser.json"
|
| 254 |
+
require = false
|
| 255 |
+
|
| 256 |
+
[initialize.components.tagger]
|
| 257 |
+
|
| 258 |
+
[initialize.components.tagger.labels]
|
| 259 |
+
@readers = "spacy.read_labels.v1"
|
| 260 |
+
path = "corpus/labels/tagger.json"
|
| 261 |
+
require = false
|
| 262 |
+
|
| 263 |
+
[initialize.lookups]
|
| 264 |
+
@misc = "spacy.LookupsDataLoader.v1"
|
| 265 |
+
lang = ${nlp.lang}
|
| 266 |
+
tables = ["lexeme_norm"]
|
| 267 |
+
|
| 268 |
+
[initialize.tokenizer]
|
en_core_web_sm-3.6.0/lemmatizer/lookups/lookups.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:eb64f40c0f8396d1762730c0ddf4dad2a52d138f5a389f71a1a1d088173b7737
|
| 3 |
+
size 972893
|
en_core_web_sm-3.6.0/meta.json
ADDED
|
@@ -0,0 +1,521 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"lang":"en",
|
| 3 |
+
"name":"core_web_sm",
|
| 4 |
+
"version":"3.6.0",
|
| 5 |
+
"description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
|
| 6 |
+
"author":"Explosion",
|
| 7 |
+
"email":"contact@explosion.ai",
|
| 8 |
+
"url":"https://explosion.ai",
|
| 9 |
+
"license":"MIT",
|
| 10 |
+
"spacy_version":">=3.6.0,<3.7.0",
|
| 11 |
+
"spacy_git_version":"cb4fdc83e",
|
| 12 |
+
"vectors":{
|
| 13 |
+
"width":0,
|
| 14 |
+
"vectors":0,
|
| 15 |
+
"keys":0,
|
| 16 |
+
"name":null
|
| 17 |
+
},
|
| 18 |
+
"labels":{
|
| 19 |
+
"tok2vec":[
|
| 20 |
+
|
| 21 |
+
],
|
| 22 |
+
"tagger":[
|
| 23 |
+
"$",
|
| 24 |
+
"''",
|
| 25 |
+
",",
|
| 26 |
+
"-LRB-",
|
| 27 |
+
"-RRB-",
|
| 28 |
+
".",
|
| 29 |
+
":",
|
| 30 |
+
"ADD",
|
| 31 |
+
"AFX",
|
| 32 |
+
"CC",
|
| 33 |
+
"CD",
|
| 34 |
+
"DT",
|
| 35 |
+
"EX",
|
| 36 |
+
"FW",
|
| 37 |
+
"HYPH",
|
| 38 |
+
"IN",
|
| 39 |
+
"JJ",
|
| 40 |
+
"JJR",
|
| 41 |
+
"JJS",
|
| 42 |
+
"LS",
|
| 43 |
+
"MD",
|
| 44 |
+
"NFP",
|
| 45 |
+
"NN",
|
| 46 |
+
"NNP",
|
| 47 |
+
"NNPS",
|
| 48 |
+
"NNS",
|
| 49 |
+
"PDT",
|
| 50 |
+
"POS",
|
| 51 |
+
"PRP",
|
| 52 |
+
"PRP$",
|
| 53 |
+
"RB",
|
| 54 |
+
"RBR",
|
| 55 |
+
"RBS",
|
| 56 |
+
"RP",
|
| 57 |
+
"SYM",
|
| 58 |
+
"TO",
|
| 59 |
+
"UH",
|
| 60 |
+
"VB",
|
| 61 |
+
"VBD",
|
| 62 |
+
"VBG",
|
| 63 |
+
"VBN",
|
| 64 |
+
"VBP",
|
| 65 |
+
"VBZ",
|
| 66 |
+
"WDT",
|
| 67 |
+
"WP",
|
| 68 |
+
"WP$",
|
| 69 |
+
"WRB",
|
| 70 |
+
"XX",
|
| 71 |
+
"_SP",
|
| 72 |
+
"``"
|
| 73 |
+
],
|
| 74 |
+
"parser":[
|
| 75 |
+
"ROOT",
|
| 76 |
+
"acl",
|
| 77 |
+
"acomp",
|
| 78 |
+
"advcl",
|
| 79 |
+
"advmod",
|
| 80 |
+
"agent",
|
| 81 |
+
"amod",
|
| 82 |
+
"appos",
|
| 83 |
+
"attr",
|
| 84 |
+
"aux",
|
| 85 |
+
"auxpass",
|
| 86 |
+
"case",
|
| 87 |
+
"cc",
|
| 88 |
+
"ccomp",
|
| 89 |
+
"compound",
|
| 90 |
+
"conj",
|
| 91 |
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"csubj",
|
| 92 |
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"csubjpass",
|
| 93 |
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"dative",
|
| 94 |
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"dep",
|
| 95 |
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"det",
|
| 96 |
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"dobj",
|
| 97 |
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"expl",
|
| 98 |
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"intj",
|
| 99 |
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"mark",
|
| 100 |
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"meta",
|
| 101 |
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"neg",
|
| 102 |
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"nmod",
|
| 103 |
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"npadvmod",
|
| 104 |
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"nsubj",
|
| 105 |
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"nsubjpass",
|
| 106 |
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"nummod",
|
| 107 |
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"oprd",
|
| 108 |
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"parataxis",
|
| 109 |
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"pcomp",
|
| 110 |
+
"pobj",
|
| 111 |
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"poss",
|
| 112 |
+
"preconj",
|
| 113 |
+
"predet",
|
| 114 |
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"prep",
|
| 115 |
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"prt",
|
| 116 |
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"punct",
|
| 117 |
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"quantmod",
|
| 118 |
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"relcl",
|
| 119 |
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"xcomp"
|
| 120 |
+
],
|
| 121 |
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"attribute_ruler":[
|
| 122 |
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|
| 123 |
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],
|
| 124 |
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"lemmatizer":[
|
| 125 |
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|
| 126 |
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],
|
| 127 |
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"ner":[
|
| 128 |
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"CARDINAL",
|
| 129 |
+
"DATE",
|
| 130 |
+
"EVENT",
|
| 131 |
+
"FAC",
|
| 132 |
+
"GPE",
|
| 133 |
+
"LANGUAGE",
|
| 134 |
+
"LAW",
|
| 135 |
+
"LOC",
|
| 136 |
+
"MONEY",
|
| 137 |
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"NORP",
|
| 138 |
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"ORDINAL",
|
| 139 |
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"ORG",
|
| 140 |
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"PERCENT",
|
| 141 |
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"PERSON",
|
| 142 |
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"PRODUCT",
|
| 143 |
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"QUANTITY",
|
| 144 |
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"TIME",
|
| 145 |
+
"WORK_OF_ART"
|
| 146 |
+
]
|
| 147 |
+
},
|
| 148 |
+
"pipeline":[
|
| 149 |
+
"tok2vec",
|
| 150 |
+
"tagger",
|
| 151 |
+
"parser",
|
| 152 |
+
"attribute_ruler",
|
| 153 |
+
"lemmatizer",
|
| 154 |
+
"ner"
|
| 155 |
+
],
|
| 156 |
+
"components":[
|
| 157 |
+
"tok2vec",
|
| 158 |
+
"tagger",
|
| 159 |
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"parser",
|
| 160 |
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"senter",
|
| 161 |
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"attribute_ruler",
|
| 162 |
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"lemmatizer",
|
| 163 |
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"ner"
|
| 164 |
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],
|
| 165 |
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"disabled":[
|
| 166 |
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"senter"
|
| 167 |
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],
|
| 168 |
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"performance":{
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| 169 |
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"token_acc":0.9986194413,
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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en_core_web_sm-3.6.0/senter/cfg
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en_core_web_sm-3.6.0/senter/model
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| 28 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 50 |
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en_core_web_sm-3.6.0/tagger/model
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|
en_core_web_sm-3.6.0/vocab/key2row
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
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|
| 1 |
+
�
|
en_core_web_sm-3.6.0/vocab/lookups.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1ddd140ecac6a8c4592e9146d6e30074569ffaed97ee51edc9587dc510f8934c
|
| 3 |
+
size 69982
|
en_core_web_sm-3.6.0/vocab/strings.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
en_core_web_sm-3.6.0/vocab/vectors
ADDED
|
Binary file (128 Bytes). View file
|
|
|
en_core_web_sm-3.6.0/vocab/vectors.cfg
ADDED
|
@@ -0,0 +1,3 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"mode":"default"
|
| 3 |
+
}
|
examples/4.png
ADDED
|
examples/5.jpg
ADDED
|
examples/6.jpg
ADDED
|
examples/example_inputs.jsonl
CHANGED
|
@@ -1,3 +1,6 @@
|
|
| 1 |
{"id":1, "text": "Describe this image", "image": "examples/1.png"}
|
| 2 |
{"id":2, "text": "What is written in the image?", "image": "examples/2.jpg"}
|
| 3 |
-
{"id":3, "text": "How many houses are there in this cartoon?", "image": "examples/3.jpg"}
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
{"id":1, "text": "Describe this image", "image": "examples/1.png"}
|
| 2 |
{"id":2, "text": "What is written in the image?", "image": "examples/2.jpg"}
|
| 3 |
+
{"id":3, "text": "How many houses are there in this cartoon?", "image": "examples/3.jpg"}
|
| 4 |
+
{"id":4, "text": "Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?", "image": "examples/4.png"}
|
| 5 |
+
{"id":5, "text": "Where is the tree closer to the sun?", "image": "examples/5.jpg"}
|
| 6 |
+
{"id":6, "text": "What color are the clothes of the girl whose hands are holding flowers? Let's think step by step", "image": "examples/6.jpg"}
|
requirements.txt
ADDED
|
@@ -0,0 +1,8 @@
|
|
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|
|
|
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|
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|
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|
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|
|
|
| 1 |
+
gradio
|
| 2 |
+
seaborn
|
| 3 |
+
PIL
|
| 4 |
+
base64
|
| 5 |
+
matplotlib
|
| 6 |
+
spacy==3.6.0
|
| 7 |
+
requests
|
| 8 |
+
hashlib
|
utils.py
ADDED
|
@@ -0,0 +1,86 @@
|
|
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|
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|
| 1 |
+
import seaborn as sns
|
| 2 |
+
from PIL import Image, ImageDraw, ImageFont
|
| 3 |
+
import matplotlib.font_manager
|
| 4 |
+
import spacy
|
| 5 |
+
import re
|
| 6 |
+
|
| 7 |
+
nlp = spacy.load("en_core_web_sm-3.6.0")
|
| 8 |
+
|
| 9 |
+
def draw_boxes(image, boxes, texts, output_fn='output.png'):
|
| 10 |
+
box_width = 5
|
| 11 |
+
color_palette = sns.color_palette("husl", len(boxes))
|
| 12 |
+
colors = [(int(r*255), int(g*255), int(b*255)) for r, g, b in color_palette]
|
| 13 |
+
|
| 14 |
+
width, height = image.size
|
| 15 |
+
absolute_boxes = [[(int(box[0] * width), int(box[1] * height), int(box[2] * width), int(box[3] * height)) for box in b] for b in boxes]
|
| 16 |
+
|
| 17 |
+
overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
|
| 18 |
+
draw = ImageDraw.Draw(overlay)
|
| 19 |
+
font_path = sorted(matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf'))[0]
|
| 20 |
+
font = ImageFont.truetype(font_path, size=26)
|
| 21 |
+
|
| 22 |
+
for box, text, color in zip(absolute_boxes, texts, colors):
|
| 23 |
+
for b in box:
|
| 24 |
+
draw.rectangle(b, outline=color, width=box_width)
|
| 25 |
+
if not text:
|
| 26 |
+
continue
|
| 27 |
+
splited_text = text.split('\n')
|
| 28 |
+
num_lines = len(splited_text)
|
| 29 |
+
text_width, text_height = font.getbbox(splited_text[0])[-2:]
|
| 30 |
+
y_start = b[3] - text_height * num_lines - box_width
|
| 31 |
+
if b[2] - b[0] < 100 or b[3] - b[1] < 100:
|
| 32 |
+
y_start = b[3]
|
| 33 |
+
for i, line in enumerate(splited_text):
|
| 34 |
+
text_width, text_height = font.getbbox(line)[-2:]
|
| 35 |
+
x = b[0] + box_width
|
| 36 |
+
y = y_start + text_height * i
|
| 37 |
+
draw.rectangle([x, y, x+text_width, y+text_height], fill=(128, 128, 128, 160))
|
| 38 |
+
draw.text((x, y), line, font=font, fill=(255, 255, 255))
|
| 39 |
+
img_with_overlay = Image.alpha_composite(image.convert('RGBA'), overlay).convert('RGB')
|
| 40 |
+
img_with_overlay.save(output_fn)
|
| 41 |
+
|
| 42 |
+
def boxstr_to_boxes(box_str):
|
| 43 |
+
boxes = [[int(y)/1000 for y in x.split(',')] for x in box_str.split(';') if x.replace(',', '').isdigit()]
|
| 44 |
+
return boxes
|
| 45 |
+
|
| 46 |
+
def text_to_dict(text):
|
| 47 |
+
doc = nlp(text)
|
| 48 |
+
|
| 49 |
+
box_matches = list(re.finditer(r'\[\[([^\]]+)\]\]', text))
|
| 50 |
+
box_positions = [match.start() for match in box_matches]
|
| 51 |
+
|
| 52 |
+
noun_phrases = []
|
| 53 |
+
boxes = []
|
| 54 |
+
|
| 55 |
+
for match, box_position in zip(box_matches, box_positions):
|
| 56 |
+
nearest_np_start = max([0] + [chunk.start_char for chunk in doc.noun_chunks if chunk.end_char <= box_position])
|
| 57 |
+
noun_phrase = text[nearest_np_start:box_position].strip()
|
| 58 |
+
if noun_phrase and noun_phrase[-1] == '?':
|
| 59 |
+
noun_phrase = text[:box_position].strip()
|
| 60 |
+
box_string = match.group(1)
|
| 61 |
+
|
| 62 |
+
noun_phrases.append(noun_phrase)
|
| 63 |
+
boxes.append(boxstr_to_boxes(box_string))
|
| 64 |
+
|
| 65 |
+
pairs = []
|
| 66 |
+
for noun_phrase, box_string in zip(noun_phrases, boxes):
|
| 67 |
+
pairs.append((noun_phrase.lower(), box_string))
|
| 68 |
+
return dict(pairs)
|
| 69 |
+
|
| 70 |
+
def parse_response(img, response, output_fn='output.png'):
|
| 71 |
+
img = img.convert('RGB')
|
| 72 |
+
width, height = img.size
|
| 73 |
+
ratio = min(1920 / width, 1080 / height)
|
| 74 |
+
new_width = int(width * ratio)
|
| 75 |
+
new_height = int(height * ratio)
|
| 76 |
+
new_img = img.resize((new_width, new_height), Image.LANCZOS)
|
| 77 |
+
pattern = r"\[\[(.*?)\]\]"
|
| 78 |
+
positions = re.findall(pattern, response)
|
| 79 |
+
boxes = [[[int(y) for y in x.split(',')] for x in pos.split(';') if x.replace(',', '').isdigit()] for pos in positions]
|
| 80 |
+
dic = text_to_dict(response)
|
| 81 |
+
if not dic:
|
| 82 |
+
texts = []
|
| 83 |
+
boxes = []
|
| 84 |
+
else:
|
| 85 |
+
texts, boxes = zip(*dic.items())
|
| 86 |
+
draw_boxes(new_img, boxes, texts, output_fn=output_fn)
|