first commit

README.md

@@ -1,107 +1,18 @@
 ---
-pipeline_tag: sentence-similarity
-license: apache-2.0
+language:
+- en  # Example: en
+license: MIT  # Example: apache-2.0 or any license from https://hf.co/docs/hub/model-repos#list-of-license-identifiers
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
+- text-generation
+datasets:
+- waiting-messages  # Example: common_voice. Use dataset id from https://hf.co/datasets
+widget:
+- text: 'List of funny waiting messages:'
+  example_title: 'Funny waiting messages'
 ---
 
-# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+# Langame/gpt2-waiting
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This fine-tuned model can generate funny waiting messages.
 
-
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, max pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-
-
-## Evaluation Results
-
-
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
-
-## Citing & Authors
-
-This model was trained by [sentence-transformers](https://www.sbert.net/). 
-        
-If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
-```bibtex 
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "http://arxiv.org/abs/1908.10084",
-}
-```
+[Langame](https://langa.me) uses these within its platform 😛.

metadata.yaml

@@ -1,24 +0,0 @@
-language: 
-  - "List of ISO 639-1 code for your language"
-  - lang1
-  - lang2
-thumbnail: "url to a thumbnail used in social sharing"
-tags:
-- tag1
-- tag2
-license: "any valid license identifier"
-datasets:
-- dataset1
-- dataset2
-metrics:
-- metric1
-- metric2
-widget:
-- text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy."
-  example_title: "Sentiment analysis"
-- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..."
-  example_title: "Coreference resolution"
-- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..."
-  example_title: "Logic puzzles"
-- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..."
-  example_title: "Reading comprehension"