Spaces:
Runtime error
Runtime error
File size: 3,758 Bytes
fffc1e9 bd5687f fffc1e9 10eaeda fffc1e9 bd639d6 fffc1e9 d4df546 6353e4e bd5687f d4df546 953205d d4df546 fffc1e9 37b391d fffc1e9 953205d fffc1e9 d4df546 fffc1e9 d4df546 fffc1e9 0bb0923 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
from evaluate_model import compute_metrics
from datasets import load_from_disk, Dataset
from transformers import AutoTokenizer
import os
import pickle
from transformers import AutoModelForTokenClassification
# from transformers import DataCollatorForTokenClassification
from utils import tokenize_and_align_labels
from rich import print
import huggingface_hub
import torch
import json
from tqdm import tqdm
# _ = load_dotenv(find_dotenv()) # read local .env file
hf_token= os.environ['HF_TOKEN']
huggingface_hub.login(hf_token)
checkpoint = 'elshehawy/finer-ord-transformers'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_path = './data/merged_dataset/'
test = load_from_disk(data_path)['test']
test = Dataset.from_dict(test[:16])
feature_path = './data/ner_feature.pickle'
with open(feature_path, 'rb') as f:
ner_feature = pickle.load(f)
# data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
ner_model = AutoModelForTokenClassification.from_pretrained(checkpoint)
# tokenized_dataset.set_format('torch')
def collate_fn(data):
input_ids = [(element['input_ids']) for element in data]
attention_mask = [element['attention_mask'] for element in data]
token_type_ids = [element['token_type_ids'] for element in data]
labels = [element['labels'] for element in data]
return input_ids, token_type_ids, attention_mask, labels
ner_model = ner_model.eval()
def get_metrics_trf(data):
# print(device)
data = Dataset.from_dict(data)
tokenized_data = data.map(
tokenize_and_align_labels,
batched=True,
batch_size=None,
remove_columns=data.column_names[2:],
fn_kwargs={'tokenizer': tokenizer}
)
loader = torch.utils.data.DataLoader(tokenized_data, batch_size=16, collate_fn=collate_fn)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
y_true, logits = [], []
for input_ids, token_type_ids, attention_mask, labels in tqdm(loader):
ner_model.to(device)
with torch.no_grad():
logits.extend(
ner_model(
input_ids=torch.tensor(input_ids).to(device),
token_type_ids=torch.tensor(token_type_ids).to(device),
attention_mask=torch.tensor(attention_mask).to(device)
).logits.cpu().numpy()
)
y_true.extend(labels)
all_metrics = compute_metrics((logits, y_true))
return all_metrics
# with open('./metrics/trf/metrics.json', 'w') as f:
# json.dump(all_metrics, f)
def find_orgs_in_data(tokens, labels):
orgs = []
prev_tok_id = 0
for i, (token, label) in enumerate(zip(tokens, labels)):
if label == 'B-ORG':
org = []
org.append(token)
orgs.append(org)
prev_tok_id = i
if label == 'I-ORG' and (i-1) == prev_tok_id:
org = orgs[-1]
org.append(token)
orgs[-1] = org
prev_tok_id = i
# print(i)
return [tokenizer.convert_tokens_to_string(org) for org in orgs]
def store_sample_data(data):
data = Dataset.from_dict(data)
test_data = []
for sent in data:
labels = [ner_feature.feature.int2str(l) for l in sent['ner_tags']]
# print(labels)
sent_orgs = find_orgs(sent['tokens'], labels)
sent_text = tokenizer.convert_tokens_to_string(sent['tokens'])
test_data.append({
'id': sent['id'],
'text': sent_text,
'orgs': sent_orgs
})
return test_data
# with open('./data/sample_data.json', 'w') as f:
# json.dump(test_data, f) |