diff --git a/convlab2/base_models/bert/create_data.py b/convlab2/base_models/bert/create_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..f85f9d1115d0607a12f01ad6a85fdc690cf32263
--- /dev/null
+++ b/convlab2/base_models/bert/create_data.py
@@ -0,0 +1,62 @@
+import os
+import json
+from tqdm import tqdm
+from convlab2.util import load_dataset, load_nlu_data, load_dst_data, load_policy_data, load_nlg_data, load_e2e_data, load_rg_data
+from nltk.tokenize import TreebankWordTokenizer, PunktSentenceTokenizer
+from collections import Counter
+
+def create_bio_data(dataset, data_dir):
+ data_by_split = load_nlu_data(dataset, speaker='all')
+ os.makedirs(data_dir, exist_ok=True)
+
+ sent_tokenizer = PunktSentenceTokenizer()
+ word_tokenizer = TreebankWordTokenizer()
+
+ data_splits = data_by_split.keys()
+ cnt = Counter()
+ for data_split in data_splits:
+ data = []
+ for sample in tqdm(data_by_split[data_split], desc=f'{data_split} sample', leave=False):
+ utterance = sample['utterance']
+ dialogue_acts = [da for da in sample['dialogue_acts']['non-categorical'] if 'start' in da]
+ cnt[len(dialogue_acts)] += 1
+
+ sentences = sent_tokenizer.tokenize(utterance)
+ sent_spans = sent_tokenizer.span_tokenize(utterance)
+ tokens = [token for sent in sentences for token in word_tokenizer.tokenize(sent)]
+ token_spans = [(sent_span[0]+token_span[0], sent_span[0]+token_span[1]) for sent, sent_span in zip(sentences, sent_spans) for token_span in word_tokenizer.span_tokenize(sent)]
+ labels = ['O'] * len(tokens)
+ for da in dialogue_acts:
+ char_start = da['start']
+ char_end = da['end']
+ word_start, word_end = -1, -1
+ for i, token_span in enumerate(token_spans):
+ if char_start == token_span[0]:
+ word_start = i
+ if char_end == token_span[1]:
+ word_end = i + 1
+ if word_start == -1 and word_end == -1:
+ # char span does not match word, skip
+ continue
+ labels[word_start] = 'B'
+ for i in range(word_start+1, word_end):
+ labels[i] = "I"
+ data.append(json.dumps({'tokens': tokens, 'labels': labels})+'\n')
+ file_name = os.path.join(data_dir, f"{data_split}.json")
+ with open(file_name, "w") as f:
+ f.writelines(data)
+ print('num of spans in utterances', cnt)
+
+if __name__ == '__main__':
+ from argparse import ArgumentParser
+ parser = ArgumentParser(description="create data for seq2seq training")
+ parser.add_argument('--tasks', metavar='task_name', nargs='*', choices=['bio'], help='names of tasks')
+ parser.add_argument('--datasets', metavar='dataset_name', nargs='*', help='names of unified datasets')
+ parser.add_argument('--save_dir', metavar='save_directory', type=str, default='data', help='directory to save the data, default: data/$task_name/$dataset_name')
+ args = parser.parse_args()
+ print(args)
+ for dataset_name in tqdm(args.datasets, desc='datasets'):
+ dataset = load_dataset(dataset_name)
+ for task_name in tqdm(args.tasks, desc='tasks', leave=False):
+ data_dir = os.path.join(args.save_dir, task_name, dataset_name)
+ eval(f"create_{task_name}_data")(dataset, data_dir)
diff --git a/convlab2/base_models/bert/run_bio.sh b/convlab2/base_models/bert/run_bio.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3d2e797c3020ea8d6e0ddcded1b900fb1141b384
--- /dev/null
+++ b/convlab2/base_models/bert/run_bio.sh
@@ -0,0 +1,49 @@
+n_gpus=8
+task_name="bio"
+dataset_name="sgd"
+data_dir="data/${task_name}/${dataset_name}"
+output_dir="output/${task_name}/${dataset_name}"
+cache_dir="cache"
+logging_dir="${output_dir}/runs"
+train_file="${data_dir}/train.json"
+validation_file="${data_dir}/validation.json"
+test_file="${data_dir}/test.json"
+source_column="tokens"
+target_column="labels"
+model_name_or_path="bert-base-uncased"
+per_device_train_batch_size=128
+per_device_eval_batch_size=512
+gradient_accumulation_steps=1
+lr=2e-5
+num_train_epochs=1
+metric_for_best_model="f1"
+
+python create_data.py --tasks ${task_name} --datasets ${dataset_name} --save_dir "data"
+
+python -m torch.distributed.launch \
+ --nproc_per_node ${n_gpus} run_token_classification.py \
+ --task_name ${task_name} \
+ --train_file ${train_file} \
+ --validation_file ${validation_file} \
+ --test_file ${test_file} \
+ --source_column ${source_column} \
+ --target_column ${target_column} \
+ --model_name_or_path ${model_name_or_path} \
+ --do_train \
+ --do_eval \
+ --do_predict \
+ --save_strategy epoch \
+ --evaluation_strategy epoch \
+ --load_best_model_at_end \
+ --metric_for_best_model ${metric_for_best_model} \
+ --cache_dir ${cache_dir} \
+ --output_dir ${output_dir} \
+ --logging_dir ${logging_dir} \
+ --overwrite_output_dir \
+ --preprocessing_num_workers 4 \
+ --per_device_train_batch_size ${per_device_train_batch_size} \
+ --per_device_eval_batch_size ${per_device_eval_batch_size} \
+ --gradient_accumulation_steps ${gradient_accumulation_steps} \
+ --learning_rate ${lr} \
+ --num_train_epochs ${num_train_epochs} \
+ --debug underflow_overflow
diff --git a/convlab2/base_models/bert/run_token_classification.py b/convlab2/base_models/bert/run_token_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..57e156d768b3ebf84514cc73187f7092d5e7082a
--- /dev/null
+++ b/convlab2/base_models/bert/run_token_classification.py
@@ -0,0 +1,598 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for token classification.
+Modified from https://github.com/huggingface/transformers/blob/master/examples/pytorch/token-classification/run_ner.py
+"""
+# You can also adapt this script on your own token classification task and datasets. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+import json
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import numpy as np
+from datasets import ClassLabel, load_dataset, load_metric
+
+import transformers
+from transformers import (
+ AutoConfig,
+ AutoModelForTokenClassification,
+ AutoTokenizer,
+ DataCollatorForTokenClassification,
+ HfArgumentParser,
+ PreTrainedTokenizerFast,
+ Trainer,
+ TrainingArguments,
+ set_seed,
+)
+from transformers.trainer_utils import EvalPrediction, get_last_checkpoint
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.12.5")
+
+require_version("datasets>=1.16.1")
+
+logger = logging.getLogger(__name__)
+os.environ["WANDB_DISABLED"] = "true"
+
+
+@dataclass
+class ModelArguments:
+ """
+ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+ """
+
+ model_name_or_path: str = field(
+ metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+ )
+ config_name: Optional[str] = field(
+ default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+ )
+ tokenizer_name: Optional[str] = field(
+ default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+ )
+ cache_dir: Optional[str] = field(
+ default=None,
+ metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+ )
+ model_revision: str = field(
+ default="main",
+ metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+ )
+ use_auth_token: bool = field(
+ default=False,
+ metadata={
+ "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+ "with private models)."
+ },
+ )
+
+
+@dataclass
+class DataTrainingArguments:
+ """
+ Arguments pertaining to what data we are going to input our model for training and eval.
+ """
+
+
+ task_name: Optional[str] = field(
+ default=None, metadata={"help": "The name of the task (ner, pos...)."}
+ )
+ dataset_name: Optional[str] = field(
+ default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+ )
+ dataset_config_name: Optional[str] = field(
+ default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+ )
+ source_column: Optional[str] = field(
+ default=None,
+ metadata={"help": "The name of the column in the datasets containing the source texts."},
+ )
+ target_column: Optional[str] = field(
+ default=None,
+ metadata={"help": "The name of the column in the datasets containing the target labels."},
+ )
+ train_file: Optional[str] = field(
+ default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
+ )
+ validation_file: Optional[str] = field(
+ default=None,
+ metadata={
+ "help": "An optional input evaluation data file to evaluate the metrics on (a jsonlines or csv file)."
+ },
+ )
+ test_file: Optional[str] = field(
+ default=None,
+ metadata={
+ "help": "An optional input test data file to evaluate the metrics on (a jsonlines or csv file)."
+ },
+ )
+ overwrite_cache: bool = field(
+ default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+ )
+ preprocessing_num_workers: Optional[int] = field(
+ default=None,
+ metadata={"help": "The number of processes to use for the preprocessing."},
+ )
+ max_seq_length: int = field(
+ default=None,
+ metadata={
+ "help": "The maximum total input sequence length after tokenization. If set, sequences longer "
+ "than this will be truncated, sequences shorter will be padded."
+ },
+ )
+ pad_to_max_length: bool = field(
+ default=False,
+ metadata={
+ "help": "Whether to pad all samples to model maximum sentence length. "
+ "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
+ "efficient on GPU but very bad for TPU."
+ },
+ )
+ max_train_samples: Optional[int] = field(
+ default=None,
+ metadata={
+ "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+ "value if set."
+ },
+ )
+ max_eval_samples: Optional[int] = field(
+ default=None,
+ metadata={
+ "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+ "value if set."
+ },
+ )
+ max_predict_samples: Optional[int] = field(
+ default=None,
+ metadata={
+ "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+ "value if set."
+ },
+ )
+ label_all_tokens: bool = field(
+ default=False,
+ metadata={
+ "help": "Whether to put the label for one word on all tokens of generated by that word or just on the "
+ "one (in which case the other tokens will have a padding index)."
+ },
+ )
+ return_entity_level_metrics: bool = field(
+ default=False,
+ metadata={"help": "Whether to return all the entity levels during evaluation or just the overall ones."},
+ )
+
+ def __post_init__(self):
+ if (
+ self.dataset_name is None
+ and self.train_file is None
+ and self.validation_file is None
+ and self.test_file is None
+ ):
+ raise ValueError("Need either a dataset name or a training/validation/testing file.")
+ else:
+ if self.train_file is not None:
+ extension = self.train_file.split(".")[-1]
+ assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+ if self.validation_file is not None:
+ extension = self.validation_file.split(".")[-1]
+ assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+ if self.test_file is not None:
+ extension = self.test_file.split(".")[-1]
+ assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
+
+
+def main():
+ # See all possible arguments in src/transformers/training_args.py
+ # or by passing the --help flag to this script.
+ # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+ parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+ if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+ # If we pass only one argument to the script and it's the path to a json file,
+ # let's parse it to get our arguments.
+ model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+ else:
+ model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+ # Setup logging
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ handlers=[logging.StreamHandler(sys.stdout)],
+ )
+ log_level = training_args.get_process_log_level()
+ logger.setLevel(log_level)
+ datasets.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.set_verbosity(log_level)
+ transformers.utils.logging.enable_default_handler()
+ transformers.utils.logging.enable_explicit_format()
+
+ # Log on each process the small summary:
+ logger.warning(
+ f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+ )
+ logger.info(f"Training/evaluation parameters {training_args}")
+
+ # Detecting last checkpoint.
+ last_checkpoint = None
+ if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+ last_checkpoint = get_last_checkpoint(training_args.output_dir)
+ if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+ raise ValueError(
+ f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+ "Use --overwrite_output_dir to overcome."
+ )
+ elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+ logger.info(
+ f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+ "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+ )
+
+ # Set seed before initializing model.
+ set_seed(training_args.seed)
+
+ # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+ # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+ # (the dataset will be downloaded automatically from the datasets Hub).
+ #
+ # For CSV/JSON files this script will use the first column for the source texts and the second column for the
+ # target labels (unless you specify column names for this with the `source_column` and `target_column` arguments).
+ #
+ # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+ # download the dataset.
+ if data_args.dataset_name is not None:
+ # Downloading and loading a dataset from the hub.
+ raw_datasets = load_dataset(
+ data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
+ )
+ else:
+ data_files = {}
+ if data_args.train_file is not None:
+ data_files["train"] = data_args.train_file
+ extension = data_args.train_file.split(".")[-1]
+ if data_args.validation_file is not None:
+ data_files["validation"] = data_args.validation_file
+ extension = data_args.validation_file.split(".")[-1]
+ if data_args.test_file is not None:
+ data_files["test"] = data_args.test_file
+ extension = data_args.test_file.split(".")[-1]
+ raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
+ # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+ # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+ # must provide train dataset for label space
+ column_names = raw_datasets["train"].column_names
+ features = raw_datasets["train"].features
+
+ if data_args.source_column is None:
+ source_column = column_names[0]
+ else:
+ source_column = data_args.source_column
+ if source_column not in column_names:
+ raise ValueError(
+ f"--source_column' value '{data_args.source_column}' needs to be one of: {', '.join(column_names)}"
+ )
+
+ if data_args.target_column is None:
+ target_column = column_names[1]
+ else:
+ target_column = data_args.target_column
+ if target_column not in column_names:
+ raise ValueError(
+ f"--target_column' value '{data_args.target_column}' needs to be one of: {', '.join(column_names)}"
+ )
+
+ # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
+ # unique labels.
+ def get_label_list(labels):
+ unique_labels = set()
+ for label in labels:
+ unique_labels = unique_labels | set(label)
+ label_list = list(unique_labels)
+ label_list.sort()
+ return label_list
+
+ if isinstance(features[target_column].feature, ClassLabel):
+ label_list = features[target_column].feature.names
+ # No need to convert the labels since they are already ints.
+ label_to_id = {i: i for i in range(len(label_list))}
+ else:
+ label_list = get_label_list(raw_datasets["train"][target_column])
+ label_to_id = {l: i for i, l in enumerate(label_list)}
+ num_labels = len(label_list)
+
+ # Map that sends B-Xxx label to its I-Xxx counterpart for label_all_tokens
+ b_to_i_label = []
+ for idx, label in enumerate(label_list):
+ if label.startswith("B-") and label.replace("B-", "I-") in label_list:
+ b_to_i_label.append(label_list.index(label.replace("B-", "I-")))
+ else:
+ b_to_i_label.append(idx)
+
+ # Load pretrained model and tokenizer
+ #
+ # Distributed training:
+ # The .from_pretrained methods guarantee that only one local process can concurrently
+ # download model & vocab.
+ config = AutoConfig.from_pretrained(
+ model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+ num_labels=num_labels,
+ label2id=label_to_id,
+ id2label={i: l for l, i in label_to_id.items()},
+ finetuning_task=data_args.task_name,
+ cache_dir=model_args.cache_dir,
+ revision=model_args.model_revision,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+
+ tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
+ if config.model_type in {"gpt2", "roberta"}:
+ tokenizer = AutoTokenizer.from_pretrained(
+ tokenizer_name_or_path,
+ cache_dir=model_args.cache_dir,
+ use_fast=True,
+ revision=model_args.model_revision,
+ use_auth_token=True if model_args.use_auth_token else None,
+ add_prefix_space=True,
+ )
+ else:
+ tokenizer = AutoTokenizer.from_pretrained(
+ tokenizer_name_or_path,
+ cache_dir=model_args.cache_dir,
+ use_fast=True,
+ revision=model_args.model_revision,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+
+ model = AutoModelForTokenClassification.from_pretrained(
+ model_args.model_name_or_path,
+ from_tf=bool(".ckpt" in model_args.model_name_or_path),
+ config=config,
+ cache_dir=model_args.cache_dir,
+ revision=model_args.model_revision,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+
+ # Tokenizer check: this script requires a fast tokenizer.
+ if not isinstance(tokenizer, PreTrainedTokenizerFast):
+ raise ValueError(
+ "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
+ "at https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet this "
+ "requirement"
+ )
+
+ # Preprocessing the dataset
+ # Padding strategy
+ padding = "max_length" if data_args.pad_to_max_length else False
+
+ # Tokenize all texts and align the labels with them.
+ def tokenize_and_align_labels(examples):
+ tokenized_inputs = tokenizer(
+ examples[source_column],
+ padding=padding,
+ truncation=True,
+ max_length=data_args.max_seq_length,
+ # We use this argument because the texts in our dataset are lists of words (with a label for each word).
+ is_split_into_words=True,
+ )
+ labels = []
+ for i, label in enumerate(examples[target_column]):
+ word_ids = tokenized_inputs.word_ids(batch_index=i)
+ previous_word_idx = None
+ label_ids = []
+ for word_idx in word_ids:
+ # Special tokens have a word id that is None. We set the label to -100 so they are automatically
+ # ignored in the loss function.
+ if word_idx is None:
+ label_ids.append(-100)
+ # We set the label for the first token of each word.
+ elif word_idx != previous_word_idx:
+ label_ids.append(label_to_id[label[word_idx]])
+ # For the other tokens in a word, we set the label to either the current label or -100, depending on
+ # the label_all_tokens flag.
+ else:
+ if data_args.label_all_tokens:
+ label_ids.append(b_to_i_label[label_to_id[label[word_idx]]])
+ else:
+ label_ids.append(-100)
+ previous_word_idx = word_idx
+
+ labels.append(label_ids)
+ tokenized_inputs["labels"] = labels
+ return tokenized_inputs
+
+ if training_args.do_train:
+ if "train" not in raw_datasets:
+ raise ValueError("--do_train requires a train dataset")
+ train_dataset = raw_datasets["train"]
+ if data_args.max_train_samples is not None:
+ train_dataset = train_dataset.select(range(data_args.max_train_samples))
+ with training_args.main_process_first(desc="train dataset map pre-processing"):
+ train_dataset = train_dataset.map(
+ tokenize_and_align_labels,
+ batched=True,
+ num_proc=data_args.preprocessing_num_workers,
+ load_from_cache_file=not data_args.overwrite_cache,
+ desc="Running tokenizer on train dataset",
+ )
+
+ if training_args.do_eval:
+ if "validation" not in raw_datasets:
+ raise ValueError("--do_eval requires a validation dataset")
+ eval_dataset = raw_datasets["validation"]
+ if data_args.max_eval_samples is not None:
+ eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
+ with training_args.main_process_first(desc="validation dataset map pre-processing"):
+ eval_dataset = eval_dataset.map(
+ tokenize_and_align_labels,
+ batched=True,
+ num_proc=data_args.preprocessing_num_workers,
+ load_from_cache_file=not data_args.overwrite_cache,
+ desc="Running tokenizer on validation dataset",
+ )
+
+ if training_args.do_predict:
+ if "test" not in raw_datasets:
+ raise ValueError("--do_predict requires a test dataset")
+ predict_dataset = raw_datasets["test"]
+ if data_args.max_predict_samples is not None:
+ predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
+ with training_args.main_process_first(desc="prediction dataset map pre-processing"):
+ predict_dataset = predict_dataset.map(
+ tokenize_and_align_labels,
+ batched=True,
+ num_proc=data_args.preprocessing_num_workers,
+ load_from_cache_file=not data_args.overwrite_cache,
+ desc="Running tokenizer on prediction dataset",
+ )
+
+ # Data collator
+ data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
+
+ # Metrics
+ metric = load_metric("seqeval")
+
+ def compute_metrics(p: EvalPrediction):
+ predictions, labels = p
+ predictions = np.argmax(predictions, axis=2)
+
+ # Remove ignored index (special tokens)
+ true_predictions = [
+ [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
+ for prediction, label in zip(predictions, labels)
+ ]
+ true_labels = [
+ [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
+ for prediction, label in zip(predictions, labels)
+ ]
+
+ results = metric.compute(predictions=true_predictions, references=true_labels)
+ if data_args.return_entity_level_metrics:
+ # Unpack nested dictionaries
+ final_results = {}
+ for key, value in results.items():
+ if isinstance(value, dict):
+ for n, v in value.items():
+ final_results[f"{key}_{n}"] = v
+ else:
+ final_results[key] = value
+ return final_results
+ else:
+ return {
+ "precision": results["overall_precision"],
+ "recall": results["overall_recall"],
+ "f1": results["overall_f1"],
+ "accuracy": results["overall_accuracy"],
+ }
+
+ # Initialize our Trainer
+ trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=train_dataset if training_args.do_train else None,
+ eval_dataset=eval_dataset if training_args.do_eval else None,
+ tokenizer=tokenizer,
+ data_collator=data_collator,
+ compute_metrics=compute_metrics,
+ )
+
+ # Training
+ if training_args.do_train:
+ checkpoint = None
+ if training_args.resume_from_checkpoint is not None:
+ checkpoint = training_args.resume_from_checkpoint
+ elif last_checkpoint is not None:
+ checkpoint = last_checkpoint
+ train_result = trainer.train(resume_from_checkpoint=checkpoint)
+ trainer.save_model() # Saves the tokenizer too for easy upload
+
+ metrics = train_result.metrics
+ max_train_samples = (
+ data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+ )
+ metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+ trainer.log_metrics("train", metrics)
+ trainer.save_metrics("train", metrics)
+ trainer.save_state()
+
+ # Evaluation
+ if training_args.do_eval:
+ logger.info("*** Evaluate ***")
+
+ metrics = trainer.evaluate()
+
+ max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+ metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+ trainer.log_metrics("eval", metrics)
+ trainer.save_metrics("eval", metrics)
+
+ # Predict
+ if training_args.do_predict:
+ logger.info("*** Predict ***")
+
+ predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
+ predictions = np.argmax(predictions, axis=2)
+
+ # Remove ignored index (special tokens)
+ true_predictions = [
+ [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
+ for prediction, label in zip(predictions, labels)
+ ]
+
+ trainer.log_metrics("predict", metrics)
+ trainer.save_metrics("predict", metrics)
+
+ # Save predictions
+ output_predictions_file = os.path.join(training_args.output_dir, "predictions.json")
+ if trainer.is_world_process_zero():
+ with open(output_predictions_file, "w") as writer:
+ for sample, prediction in zip(raw_datasets["test"], true_predictions):
+ sample["predictions"] = prediction
+ writer.write(json.dumps(sample)+'\n')
+
+ kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": data_args.task_name}
+ if data_args.dataset_name is not None:
+ kwargs["dataset_tags"] = data_args.dataset_name
+ if data_args.dataset_config_name is not None:
+ kwargs["dataset_args"] = data_args.dataset_config_name
+ kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+ else:
+ kwargs["dataset"] = data_args.dataset_name
+
+ if training_args.push_to_hub:
+ trainer.push_to_hub(**kwargs)
+ else:
+ trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+ # For xla_spawn (TPUs)
+ main()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/setup.py b/setup.py
index 3fe3b3c914f71c0db87bf5217930bfd3b6831c45..4d57bbbeb8141fea33d80812d1cf0a6b00bfc546 100755
--- a/setup.py
+++ b/setup.py
@@ -42,6 +42,7 @@ setup(
'torch>=1.6',
'transformers>=4.0',
'datasets>=1.8',
+ 'seqeval',
'spacy',
'allennlp',
'simplejson',