arubenruben commited on
Commit
8138459
1 Parent(s): 94a8c79

commit files to HF hub

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Files changed (5) hide show
  1. config.json +9 -0
  2. model.safetensors +1 -1
  3. srl.py +130 -0
  4. tokenizer.json +2 -14
  5. tokenizer_config.json +0 -1
config.json CHANGED
@@ -7,6 +7,15 @@
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  "attention_probs_dropout_prob": 0.1,
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  "conv_act": "gelu",
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  "conv_kernel_size": 3,
 
 
 
 
 
 
 
 
 
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  "hidden_act": "gelu",
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 1536,
 
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  "attention_probs_dropout_prob": 0.1,
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  "conv_act": "gelu",
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  "conv_kernel_size": 3,
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+ "custom_pipelines": {
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+ "srl": {
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+ "impl": "srl.SRLPipeline",
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+ "pt": [
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+ "AutoModelForTokenClassification"
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+ ],
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+ "tf": []
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+ }
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+ },
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  "hidden_act": "gelu",
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 1536,
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:2117dc80bfc406c73f840cc9554f4ef46a3bd154e38d9284bae29df56d086a6a
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  size 3538797348
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:0fab04aac24659f8d8f0b8c1d15aad9d9727aa5142b2173e454468e3b5900971
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  size 3538797348
srl.py ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import spacy
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+ import numpy as np
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+ from transformers import Pipeline
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+
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+
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+ class SRLPipeline(Pipeline):
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+
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+ def __init__(self, *args, **kwargs):
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+ super().__init__(*args, **kwargs)
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+
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+ spacy.prefer_gpu()
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+
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+ if not spacy.util.is_package("pt_core_news_sm"):
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+ spacy.cli.download("pt_core_news_sm")
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+
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+ self.nlp = spacy.load("pt_core_news_sm")
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+
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+ def align_labels_with_tokens(self, tokenized_inputs, all_labels):
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+ results = []
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+
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+ for i, labels in enumerate(all_labels):
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+ word_ids = tokenized_inputs.word_ids(batch_index=i)
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+ type_ids = tokenized_inputs[i].type_ids
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+
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+ num_special_tokens = len(
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+ [type_id for type_id in type_ids if type_id != 0])
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+
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+ if num_special_tokens > 0:
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+ word_ids = word_ids[:-num_special_tokens]
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+
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+ new_labels = []
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+ current_word = None
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+
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+ for word_id in word_ids:
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+
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+ if word_id != current_word:
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+ # Start of a new word!
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+ current_word = word_id
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+ label = -100 if word_id is None else labels[word_id]
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+ new_labels.append(label)
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+ elif word_id is None:
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+ # Special token
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+ new_labels.append(-100)
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+ else:
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+ """
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+ # Same word as previous token
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+ label = labels[word_id]
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+ # If the label is B-XXX we change it to I-XXX
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+ if label % 2 == 1:
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+ label += 1
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+ """
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+ new_labels.append(-100)
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+
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+ results.append(new_labels)
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+
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+ tokenized_inputs['labels'] = results
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+
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+ return tokenized_inputs
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+
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+ def _sanitize_parameters(self, **kwargs):
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+ preprocess_kwargs = {}
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+
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+ if "verb" in kwargs:
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+ preprocess_kwargs["verb"] = kwargs["verb"]
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+
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+ return preprocess_kwargs, {}, {}
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+
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+ def preprocess(self, text):
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+
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+ self.text = text
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+
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+ doc = self.nlp(text.strip())
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+
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+ self.label_names = self.model.config.id2label
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+
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+ # Extract list with verbs from the text
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+ self.verbs = [token.text for token in doc if token.pos_ == "VERB"]
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+
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+ results = []
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+
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+ tokenized_input = [token.text for token in doc]
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+ raw_labels = [0] * len(tokenized_input)
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+
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+ for verb in self.verbs:
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+ tokenized_results = self.tokenizer(
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+ tokenized_input, [verb], truncation=True,
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+ is_split_into_words=True,
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+ return_tensors="pt", max_length=self.model.config.max_position_embeddings)
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+
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+ tokenized_results = self.align_labels_with_tokens(
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+ tokenized_inputs=tokenized_results, all_labels=[raw_labels])
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+
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+ self.labels = tokenized_results["labels"]
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+
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+ # Remove labels temporarily to avoid conflicts in the forward pass
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+ tokenized_results.pop("labels")
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+
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+ results.append(tokenized_results)
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+
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+ return results
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+
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+ def _forward(self, batch_inputs):
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+ results = []
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+
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+ for entry in batch_inputs:
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+ results.append(self.model(**entry))
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+
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+ return results
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+
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+ def postprocess(self, batch_outputs):
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+ outputs = []
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+
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+ for i, entry in enumerate(batch_outputs):
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+ logits = entry.logits
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+
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+ predictions = np.argmax(logits, axis=-1).squeeze().tolist()
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+
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+ true_predictions = []
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+
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+ for prediction, label in zip(predictions, self.labels[0]):
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+ if label != -100:
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+ true_predictions.append(self.label_names[prediction])
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+
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+ outputs.append({
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+ "tokens": self.text.split(),
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+ "predictions": true_predictions,
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+ "verb": self.verbs[i]
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+ })
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+
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+ return outputs
tokenizer.json CHANGED
@@ -1,19 +1,7 @@
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  {
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  "version": "1.0",
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- "truncation": {
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- "direction": "Right",
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- "max_length": 512,
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- "strategy": "LongestFirst",
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- "stride": 0
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- },
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- "padding": {
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- "strategy": "BatchLongest",
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- "direction": "Right",
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- "pad_to_multiple_of": null,
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- "pad_id": 0,
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- "pad_type_id": 0,
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- "pad_token": "[PAD]"
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- },
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  "added_tokens": [
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  {
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  "id": 0,
 
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  {
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  "version": "1.0",
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+ "truncation": null,
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+ "padding": null,
 
 
 
 
 
 
 
 
 
 
 
 
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  "added_tokens": [
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  {
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  "id": 0,
tokenizer_config.json CHANGED
@@ -1,5 +1,4 @@
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  {
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- "add_prefix_space": true,
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  "added_tokens_decoder": {
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  "0": {
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  "content": "[PAD]",
 
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  {
 
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  "added_tokens_decoder": {
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  "0": {
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  "content": "[PAD]",