def __init__(self,
model_path: str = HF_MODEL_PATH,
no_product_labels: bool = False):
if model_path == HF_MODEL_PATH:
self.model = DistilBertForTokenClassification.from_pretrained(
HF_MODEL_PATH)
else:
self.model = DistilBertForTokenClassification.from_pretrained(
model_path)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer = get_tokenizer()
self.label_dict = id2tag if no_product_labels == False else id2tag_no_prod
self.model.to(self.device)
self.model.eval()
def __init__(self, model_path, tag_path):
with open(tag_path, "r") as tag_file:
file_content = tag_file.read().strip()
self.id_to_tag = file_content.splitlines()
self.model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(self.id_to_tag))
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
self.tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
def __init__(self, model_path=None, use_cuda=False):
if not model_path:
model_path = get_model_path()
if not os.path.exists(model_path):
raise FileNotFoundError("Cannot find model under " + model_path)
self.device = "cuda" if use_cuda and torch.cuda.is_available() else "cpu"
self.model = DistilBertForTokenClassification.from_pretrained(model_path)
self.model.to(self.device)
self.tokenizer = DistilBertTokenizerFast.from_pretrained(model_path)
self.label_map = self.get_label_map(model_path)
def retrain(epochs_per_item=2, min_to_train=5):
global current_model
global currently_training
global new_annotation_count
global labeled_data
if currently_training:
"skipping while model already training"
return
if len(labeled_data) < min_to_train:
print("too few annotations to train: "+str(len(labeled_data)))
return
currently_training = True
new_annotation_count = 0
new_model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=5)
for epoch in range(0, epochs_per_item):
print("epoch "+str(epoch))
shuffle(labeled_data)
for report in labeled_data:
annotations = json.loads(report[8])
report_text = report[1]
train_item(new_model, annotations, report_text)
eel.sleep(0.01) # allow other processes through
'''
MODEL EVALUATION CODE HERE IF YOU WANT TO TEST THAT IT IS GETTING BETTER
'''
current_model = new_model
timestamp = re.sub('\.[0-9]*','_',str(datetime.now())).replace(" ", "_").replace("-", "").replace(":","")
number_items = str(len(labeled_data))
model_path = "models/"+timestamp+number_items+".model"
current_model.save_pretrained(model_path)
if verbose:
print("saved model to "+model_path)
clean_old_models()
currently_training = False
def train(dataset_path, tag_path, model_save_path):
texts, tags = read_dataset(dataset_path)
#train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2, shuffle=True)
with open(tag_path, "r") as tag_file:
content = tag_file.read().strip()
unique_tags = content.splitlines()
tag_to_id = {tag: id for id, tag in enumerate(unique_tags)}
tokenizer = DistilBertTokenizerFast.from_pretrained(
'distilbert-base-cased')
train_encodings = tokenizer(texts,
is_split_into_words=True,
return_offsets_mapping=True,
padding=True,
truncation=True)
#val_encodings = tokenizer(val_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
train_labels = encode_tags(tags, train_encodings, tag_to_id)
#val_labels = encode_tags(val_tags, val_encodings, tag_to_id)
train_encodings.pop(
"offset_mapping") # we don't want to pass this to the model
#val_encodings.pop("offset_mapping")
train_dataset = NERDataset(train_encodings, train_labels)
#val_dataset = NERDataset(val_encodings, val_labels)
model = DistilBertForTokenClassification.from_pretrained(
'distilbert-base-cased', num_labels=len(unique_tags))
training_args = TrainingArguments(
output_dir='./results', # output directory,
overwrite_output_dir=True,
num_train_epochs=100, # total number of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=10)
trainer = Trainer(
model=
model, # the instantiated 🤗 Transformers model to be trained 💋
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
#eval_dataset=val_dataset # evaluation dataset
)
model.train()
trainer.train()
#print("eval", trainer.evaluate())
trainer.save_model(model_save_path)
def __init__(self,
num_labels,
model_name,
output_hidden_states=False,
output_attentions=False,
batch_first=True,
use_crf=True):
super(TokenDistilBERT, self).__init__()
self.num_labels = num_labels
self.batch_first = batch_first
self.use_crf = use_crf
self.tokendistilbert = DistilBertForTokenClassification.from_pretrained(
model_name,
num_labels=self.num_labels,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions)
if self.use_crf:
self.crf = CRF(self.num_labels, batch_first=self.batch_first)
def makeMultilabelModel(self, modelName, num_labels=10, root='', **kwargs):
if modelName in [
'distilbert-base-uncased', 'distilbert2/', 'distilbert3/'
]:
print(root)
tokenizer = DistilBertTokenizerFast.from_pretrained(
'distilbert-base-uncased')
model = DistilBertForTokenClassification.from_pretrained(
root + modelName, num_labels=num_labels, **kwargs)
if modelName == 'bertweet':
tokenizer = AutoTokenizer.from_pretrained('vinai/bertweet-base')
model = AutoModelForTokenClassification.from_pretrained(
root + "vinai/bertweet-base", num_labels=num_labels, **kwargs)
if modelName == 'distilroberta-base':
tokenizer = AutoTokenizer.from_pretrained('distilroberta-base',
add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained(
root + "distilroberta-base", num_labels=num_labels, **kwargs)
if modelName == 'lstm':
tokenizer = AutoTokenizer.from_pretrained(
'distilbert-base-uncased')
model = LSTMTagger(128, 64, 2, tokenizer.vocab_size, num_labels)
if modelName == 'albert-base-v2':
tokenizer = AutoTokenizer.from_pretrained('albert-base-v2',
add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained(
root + "albert-base-v2", num_labels=num_labels, **kwargs)
if modelName in 'squeezebert/squeezebert-uncased':
tokenizer = AutoTokenizer.from_pretrained(
'squeezebert/squeezebert-uncased', add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained(
root + "squeezebert/squeezebert-uncased",
num_labels=num_labels,
**kwargs)
if modelName == 'xlnet-base-cased':
tokenizer = AutoTokenizer.from_pretrained('xlnet-base-cased',
add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained(
root + "xlnet-base-cased", num_labels=num_labels, **kwargs)
return tokenizer, model
def load_existing_model():
global current_model
model_path = ""
files = os.listdir('models')
for file_name in files:
if file_name.endswith(".model"):
model_path = 'models/'+file_name
if model_path != '':
if verbose:
print("Loading model from "+model_path)
current_model = DistilBertForSequenceClassification.from_pretrained(model_path, num_labels=5)
eel.sleep(0.1)
# get_predictions()
else:
if verbose:
print("Creating new uninitialized model (OK to ignore warnings)")
current_model = DistilBertForTokenClassification.from_pretrained('distilbert-base-uncased', num_labels=5)
def test_model(model_path, tag_path):
with open(tag_path, "r") as tag_file:
file_content = tag_file.read().strip()
id_to_tag = file_content.splitlines()
model = DistilBertForTokenClassification.from_pretrained(
'distilbert-base-cased', num_labels=15)
model.load_state_dict(torch.load(model_path))
model.eval()
tokenizer = DistilBertTokenizerFast.from_pretrained(
'distilbert-base-cased')
run = True
while run:
print("Input sentence to test:")
text = input("> ")
encoded = tokenizer(text,
is_split_into_words=False,
return_offsets_mapping=True,
padding=True,
truncation=True,
return_tensors="pt")
output = model(encoded.input_ids, encoded.attention_mask)
logits = output.logits
logits_softmax = torch.nn.Softmax(dim=2)(logits).detach().cpu()
entities = []
for token_index in range(logits_softmax.shape[1]):
max_id = torch.argmax(logits_softmax[0, token_index, :]).numpy()
max_id_value = logits_softmax[0, token_index, max_id].numpy()
current_offsets = encoded.offset_mapping[0, token_index, :]
if current_offsets[0] == 0 and current_offsets[1] == 0:
continue
word = text[current_offsets[0]:current_offsets[1]]
if id_to_tag[max_id] == "O":
continue
entities.append((word, id_to_tag[max_id], max_id_value))
print("Found entities:")
for (word, tag, conf) in entities:
print(f"'{word}': {tag} (conf: {conf*100.0:.4f}%)")
val_encodings = tokenizer(validate_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
train_labels = encode_tags(train_is_argum, train_encodings,tag2id )
val_labels = encode_tags(validate_is_argum, val_encodings,tag2id )
train_encodings.pop("offset_mapping") # we don't want to pass this to the model
val_encodings.pop("offset_mapping")
train_dataset = WNUTDataset(train_encodings, train_labels)
val_dataset = WNUTDataset(val_encodings, val_labels)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
model.to(device)
model.train()
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
optim = AdamW(model.parameters(), lr=5e-5)
for epoch in range(3):
for batch in train_loader:
optim.zero_grad()
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['labels'].to(device)
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
elif args.pre_wgts == 'pubmed-full':
pre_wgts = "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext"
elif args.pre_wgts == 'pubmed-abs':
pre_wgts = "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract"
else: # args.pre_wgts == 'bert-base'
pre_wgts = "bert-base-uncased"
if args.model == 'bert':
model = BertForTokenClassification.from_pretrained(pre_wgts,
num_labels=n_tags)
if args.model == 'bert_crf':
model = BERT_CRF.from_pretrained(pre_wgts, num_labels=n_tags)
if args.model == 'bert_lstm_crf':
model = BERT_LSTM_CRF.from_pretrained(pre_wgts, num_labels=n_tags)
if args.model == 'distil':
model = DistilBertForTokenClassification.from_pretrained(pre_wgts,
num_labels=n_tags)
if args.model == 'distil_crf':
model = Distil_CRF.from_pretrained(pre_wgts, num_labels=n_tags)
model.to(device)
optimizer = AdamW(model.parameters(), lr=args.lr)
# Slanted triangular Learning rate scheduler
total_steps = len(train_loader) * args.epochs // args.accum_step
warm_steps = int(total_steps * args.warm_frac)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=warm_steps,
num_training_steps=total_steps)
#%% Train the model
if os.path.exists(args.exp_dir) == False:
def predict_spans(sentences):
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased",
do_lower_case=True)
# Load configuration
config = DistilBertConfig.from_pretrained("distilbert-base-uncased",
num_labels=3)
# Load model
bert_model = DistilBertForTokenClassification.from_pretrained(
"distilbert-base-uncased", config=config)
checkpoints = [output_dir]
for checkp in checkpoints:
model = BertLstmCrf(
bert_model,
config,
num_labels=3,
embedding_dim=config.hidden_size,
hidden_dim=int(config.hidden_size / 2),
rnn_layers=0,
# rnn_dropout=config.hidden_dropout_prob,
# output_dropout=config.hidden_dropout_prob,
use_cuda=True,
)
checkpoint = os.path.join(checkp, WEIGHTS_NAME)
state_dict = torch.load(checkpoint)
model.load_state_dict(state_dict, strict=False)
model.to("cuda:0")
del bert_model
gc.collect()
examples = [
InputExample(words=sentence.split(),
guid=[],
labels=["O" for x in sentence.split()])
for sentence in sentences
]
model_type = "distilbert"
max_seq_length = 256
pad_token_label_id = CrossEntropyLoss().ignore_index
label_list = ["O", "B-PROP", "I-PROP"]
features = convert_examples_to_features(
examples,
label_list,
max_seq_length,
tokenizer,
cls_token_at_end=bool(model_type in ["xlnet"]),
# xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=bool(model_type in ["roberta"]),
# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=bool(model_type in ["xlnet"]),
# pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if model_type in ["xlnet"] else 0,
pad_token_label_id=pad_token_label_id,
)
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long).to("cuda:0")
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long).to("cuda:0")
all_label_ids = torch.tensor([f.label_ids for f in features],
dtype=torch.long).to("cuda:0")
del features
gc.collect()
model.eval()
with torch.no_grad():
inputs = {
"input_ids": all_input_ids,
"attention_mask": all_input_mask,
"labels": all_label_ids,
}
outputs = model(**inputs)
_, _, predicted_tags = outputs
del model
gc.collect()
preds = []
# Iterate through each line of text
for x in range(all_input_ids.shape[0]):
p = []
tokens = tokenizer.convert_ids_to_tokens(all_input_ids[x])
# JUST A REMINDER:
# label "1" means start of propaganda, and "2" means the propaganda ends here.
for i in range(len(tokens)):
if tokens[i] == "[SEP]":
break
if tokens[i] == "[CLS]":
continue
p.append((tokens[i], predicted_tags[x][i]))
preds.append(p)
del all_input_ids, all_input_mask, all_label_ids
gc.collect()
return preds
## huggingface
from transformers import DistilBertTokenizerFast, DistilBertForTokenClassification, DistilBertForSequenceClassification
import torch
distil_bert = 'distilbert-base-cased'
tokenizer = DistilBertTokenizerFast.from_pretrained(distil_bert, do_lower_case=False, add_special_tokens=True,
max_length=256, pad_to_max_length=True)
token_clf = DistilBertForTokenClassification.from_pretrained(distil_bert)
sequence_clf = DistilBertForSequenceClassification.from_pretrained(distil_bert)
sentence = 'Apple and Microsoft plan to form a joint venture for the development of cloud-based computing ' \
'infrastructure.'
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
token_clf(input_ids)
outputs = model(input_ids)
last_hidden_states = outputs[0]
test = db.sample(n=10)
token_clf(tokenizer.encode_plus(sentence))
tokenizer.batch_encode_plus(test.text.to_list())
kb
## spacy
def get_sequences_with_2_orgs(text, dist=150):
''' Uses spacy NER to identify organisations. If two organizations are detected within dist
tokens from each other, extracts the sequence
'''
# Apply the model
def train(
train_data_path: str,
model_save_path: str,
prop_train: float = 0.8,
no_product_labels: bool = False,
seed: int = 9,
evaluate_after_training: bool = True,
eval_file_path: str = "../data/eval/eval_labeled.json",
):
"""
train_data_path: The path to your training data. Will be split
model_save_path: The path to where your model should be saved.
prop_train: The proportion of your training data to be held out for
calculating the loss during training.
no_product_labels: If False, removes Product tags from the training data
and converts them to O's, so the model will not learn to extract Products.
seed: Random seed to initialize the weights. I found good results with 9.
evaluate_after_training: Whether to evaluate the model immediately after
training and save the stats at `data/performance/{model_path}`.
eval_file_path: Path to a custom eval file. Note this needs to be a
LabelStudio-formatted JSON to work correctly. (See format of included
eval file.)
"""
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
with open(train_data_path) as f:
data = f.read()
train_encodings, train_labels, val_encodings, val_labels = preprocess_bio_data(
data, prop_train=prop_train, no_product_labels=no_product_labels)
train_dataset = TokenClassificationDataset(train_encodings, train_labels)
val_dataset = TokenClassificationDataset(val_encodings, val_labels)
if no_product_labels:
train_dataset.unique_tags = ["B-Ingredient", "I-Ingredient", "O"]
val_dataset.unique_tags = ["B-Ingredient", "I-Ingredient", "O"]
model = DistilBertForTokenClassification.from_pretrained(
"distilbert-base-cased", num_labels=len(train_dataset.unique_tags))
model.to(DEVICE)
training_args = TrainingArguments(
output_dir=model_save_path,
num_train_epochs=7, # total number of training epochs
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=16, # batch size for evaluation
do_eval=True,
evaluate_during_training=True,
eval_steps=10,
warmup_steps=50,
weight_decay=0.01, # strength of weight decay
overwrite_output_dir=True,
seed=seed,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
trainer.train()
trainer.save_model(model_save_path)
# Runs evaluation and saves a bunch of stats
if evaluate_after_training:
evaluate_model(
model_save_path,
eval_file_path=eval_file_path,
no_product_labels=no_product_labels,
)
print("Model has been evaluated. Results are available at "
f"../data/performance/{model_save_path.split('/')[-1]}.")
train_dataloader = DataLoader(train_data, sampler = train_sampler, batch_size = bs)
valid_data = TensorDataset(val_inputs, val_masks, val_tags)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data, sampler = valid_sampler, batch_size = bs)
test_data = TensorDataset(te_inputs, te_masks, te_tags)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler = test_sampler, batch_size = bs)
#config = DistilBertConfig.from_pretrained("distillbert_ner_c_model_save")
#model = DistillBertTagger(config = config)
model = DistilBertForTokenClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=len(tag2idx),
output_attentions = False,
output_hidden_states = False
)
#model.config.num_labels = len(tag2idx)
#model.classifier = nn.Linear(768, len(tag2idx))
#model.resize_token_embeddings(len(tokenizer))
model.to(device)
FULL_FINETUNING = False
if FULL_FINETUNING:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay_rate': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
def evaluate(dataset_path, model_weights_path, tag_path):
with open(tag_path, "r") as tag_file:
file_content = tag_file.read().strip()
id_to_tag = file_content.splitlines()
model = DistilBertForTokenClassification.from_pretrained(
'distilbert-base-cased', num_labels=len(id_to_tag))
model.load_state_dict(torch.load(model_weights_path))
model.eval()
texts, tags = read_dataset(dataset_path)
unique_tags = file_content.splitlines()
tag_to_id = {tag: id for id, tag in enumerate(unique_tags)}
tokenizer = DistilBertTokenizerFast.from_pretrained(
'distilbert-base-cased')
encodings = tokenizer(texts,
is_split_into_words=True,
return_offsets_mapping=True,
padding=True,
truncation=True)
labels = encode_tags(tags, encodings, tag_to_id)
encodings.pop("offset_mapping") # we don't want to pass this to the model
dataset = NERDataset(encodings, labels)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=128,
num_workers=4)
predictions = None
labels = None
for batch in dataloader:
result = model(batch["input_ids"], batch["attention_mask"])
if predictions is None:
predictions = result.logits.detach().cpu()
labels = batch["labels"].detach().cpu()
else:
predictions = torch.cat(
(predictions, result.logits.detach().cpu()), dim=0)
labels = torch.cat((labels, batch["labels"].detach().cpu()), dim=0)
predictions_softmax = torch.nn.Softmax(dim=2)(predictions)
labels = labels.numpy()
tp = 0
fp = 0
fn = 0
tn = 0
total_predictions = 0
for sentence_labels, sentence_predictions in zip(labels,
predictions_softmax):
max_ids = torch.argmax(sentence_predictions, dim=1).numpy()
for label, prediction in zip(sentence_labels, max_ids):
if label == -100:
continue
total_predictions += 1
if label == prediction and label != 0: # TP
tp += 1
elif label != prediction and prediction != 0: # FP
fp += 1
elif label != prediction and prediction == 0 and label != 0: # FN
fn += 1
elif prediction == 0 and label == 0:
tn += 1
else:
raise Exception("This should not happen, check your code")
precision = tp / (tp + fp)
recall = tp / (tp + fn)
accuracy = (tp + tn) / total_predictions
f1 = 2 * (precision * recall) / (precision + recall)
print(
f"Test results:\n\n\tPrecision: {precision:.6f}\n\tRecall: {recall:.6f}\n\tF1 Score: {f1:.6f}\n\tAccuracy: {accuracy*100:.3f}%"
)