Add examples/run_ner_no_trainer.py (#10902)

* Add NER example with accelerate library

* This commit contains the first (yet really unfinished)
version of a script for showing how to train HuggingFace model
with their new accelerate library.

* Fix metric calculation

* make style quality

* mv ner_no_trainer to token-classification dir

* Delete --debug flag from running script

* hf_datasets -> raw_datasets

* Make a few slight adjustments

* Add an informative comment + rewrite a help comment

* Change header

* Fix a few things

* Enforce to use fast tokenizers only

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Change bash script: python3 -> accelerate launch

* make style

* Add a few missing things (see below)

* Add a max-lenghth padding to predictions and labels to
enable accelerate gather functionality

* Add PyTorch no trainer example to the example README.md

* Remove --do-train from args as being redundant for now

* DataCollatorWithPadding -> DataCollatorForTokenClassification

* Remove some obsolete args.do_train conditions from the script

* Delete --do_train from bash running script

* Delete use_slow_tokenizer from args

* Add unintentionally removed flag --label_all_tokens

* Delete --debug flag from running script

examples/token-classification/README.md

@@ -14,10 +14,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-## Token classification
+# Token classification
 
-Fine-tuning the library models for token classification task such as Named Entity Recognition (NER) or Parts-of-speech
+## PyTorch version
-tagging (POS). The main scrip `run_ner.py` leverages the 🤗 Datasets library and the Trainer API. You can easily
+
+Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech
+tagging (POS) pr phrase extraction (CHUNKS). The main scrip `run_ner.py` leverages the 🤗 Datasets library and the Trainer API. You can easily
 customize it to your needs if you need extra processing on your datasets.
 
 It will either run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own text files for
@@ -57,6 +59,74 @@ of the script.
 
 You can find the old version of the PyTorch script [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/token-classification/run_ner.py).
 
+## Pytorch version, no Trainer
+
+Based on the script [run_ner_no_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_no_trainer.py).
+
+Like `run_ner.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on a
+token classification task, either NER, POS or CHUNKS tasks or your own data in a csv or a JSON file. The main difference is that this
+script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.
+
+It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
+or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
+the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
+after installing it:
+
+```bash
+pip install accelerate
+```
+
+then
+
+```bash
+export TASK_NAME=ner
+
+python run_ner_no_trainer.py \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+  --max_seq_length 128 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3 \
+  --output_dir /tmp/$TASK_NAME/
+```
+
+You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run
+
+```bash
+accelerate config
+```
+
+and reply to the questions asked. Then
+
+```bash
+accelerate test
+```
+
+that will check everything is ready for training. Finally, you cna launch training with
+
+```bash
+export TASK_NAME=ner
+
+accelerate launch run_ner_no_trainer.py \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+  --max_seq_length 128 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3 \
+  --output_dir /tmp/$TASK_NAME/
+```
+
+This command is the same and will work for:
+
+- a CPU-only setup
+- a setup with one GPU
+- a distributed training with several GPUs (single or multi node)
+- a training on TPUs
+
+Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
+
 ### TensorFlow version
 
 The following examples are covered in this section:

examples/token-classification/run_ner_no_trainer.py

@@ -0,0 +1,535 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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 a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library
+without using a Trainer.
+"""
+
+import argparse
+import logging
+import math
+import os
+import random
+
+import datasets
+import torch
+from datasets import ClassLabel, load_dataset, load_metric
+from torch.utils.data.dataloader import DataLoader
+from tqdm.auto import tqdm
+
+import transformers
+from accelerate import Accelerator
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_MAPPING,
+    AdamW,
+    AutoConfig,
+    AutoModelForTokenClassification,
+    AutoTokenizer,
+    DataCollatorForTokenClassification,
+    SchedulerType,
+    default_data_collator,
+    get_scheduler,
+    set_seed,
+)
+
+
+logger = logging.getLogger(__name__)
+# You should update this to your particular problem to have better documentation of `model_type`
+MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Finetune a transformers model on a text classification task (NER) with accelerate library"
+    )
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default=None,
+        help="The name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The configuration name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
+    )
+    parser.add_argument(
+        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
+    )
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=128,
+        help=(
+            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
+            " sequences shorter will be padded if `--pad_to_max_lenght` is passed."
+        ),
+    )
+    parser.add_argument(
+        "--pad_to_max_length",
+        action="store_true",
+        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+        required=True,
+    )
+    parser.add_argument(
+        "--config_name",
+        type=str,
+        default=None,
+        help="Pretrained config name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the evaluation dataloader.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-5,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--lr_scheduler_type",
+        type=SchedulerType,
+        default="linear",
+        help="The scheduler type to use.",
+        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
+    )
+    parser.add_argument(
+        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default=None,
+        help="Model type to use if training from scratch.",
+        choices=MODEL_TYPES,
+    )
+    parser.add_argument(
+        "--label_all_tokens",
+        action="store_true",
+        help="Setting labels of all special tokens to -100 and thus PyTorch will ignore them.",
+    )
+    parser.add_argument(
+        "--return_entity_level_metrics",
+        action="store_true",
+        help="Indication whether entity level metrics are to be returner.",
+    )
+    parser.add_argument(
+        "--task_name",
+        type=str,
+        default="ner",
+        choices=["ner", "pos", "chunk"],
+        help="The name of the task.",
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Activate debug mode and run training only with a subset of data.",
+    )
+    args = parser.parse_args()
+
+    # Sanity checks
+    if args.task_name is None and args.train_file is None and args.validation_file is None:
+        raise ValueError("Need either a task name or a training/validation file.")
+    else:
+        if args.train_file is not None:
+            extension = args.train_file.split(".")[-1]
+            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+        if args.validation_file is not None:
+            extension = args.validation_file.split(".")[-1]
+            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+
+    if args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    accelerator = Accelerator()
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state)
+
+    # Setup logging, we only want one process per machine to log things on the screen.
+    # accelerator.is_local_main_process is only True for one process per machine.
+    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets for token classification task 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 column called 'tokens' or the first column if no column called
+    # 'tokens' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
+    else:
+        data_files = {}
+        if args.train_file is not None:
+            data_files["train"] = args.train_file
+        if args.validation_file is not None:
+            data_files["validation"] = args.validation_file
+        extension = args.train_file.split(".")[-1]
+        raw_datasets = load_dataset(extension, data_files=data_files)
+    # Trim a number of training examples
+    if args.debug:
+        for split in raw_datasets.keys():
+            raw_datasets[split] = raw_datasets[split].select(range(100))
+    # 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.
+
+    if raw_datasets["train"] is not None:
+        column_names = raw_datasets["train"].column_names
+        features = raw_datasets["train"].features
+    else:
+        column_names = raw_datasets["validation"].column_names
+        features = raw_datasets["validation"].features
+    text_column_name = "tokens" if "tokens" in column_names else column_names[0]
+    label_column_name = f"{args.task_name}_tags" if f"{args.task_name}_tags" in column_names else column_names[1]
+
+    # 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[label_column_name].feature, ClassLabel):
+        label_list = features[label_column_name].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"][label_column_name])
+        label_to_id = {l: i for i, l in enumerate(label_list)}
+    num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if args.config_name:
+        config = AutoConfig.from_pretrained(args.config_name, num_labels=num_labels)
+    elif args.model_name_or_path:
+        config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels)
+    else:
+        config = CONFIG_MAPPING[args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
+    elif args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if args.model_name_or_path:
+        model = AutoModelForTokenClassification.from_pretrained(
+            args.model_name_or_path,
+            from_tf=bool(".ckpt" in args.model_name_or_path),
+            config=config,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = AutoModelForTokenClassification.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the raw_datasets.
+    # First we tokenize all the texts.
+    padding = "max_length" if 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[text_column_name],
+            max_length=args.max_length,
+            padding=padding,
+            truncation=True,
+            # 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[label_column_name]):
+            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:
+                    label_ids.append(label_to_id[label[word_idx]] if args.label_all_tokens else -100)
+                previous_word_idx = word_idx
+
+            labels.append(label_ids)
+        tokenized_inputs["labels"] = labels
+        return tokenized_inputs
+
+    processed_raw_datasets = raw_datasets.map(
+        tokenize_and_align_labels, batched=True, remove_columns=raw_datasets["train"].column_names
+    )
+
+    train_dataset = processed_raw_datasets["train"]
+    eval_dataset = processed_raw_datasets["validation"]
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(train_dataset)), 3):
+        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # DataLoaders creation:
+    if args.pad_to_max_length:
+        # If padding was already done ot max length, we use the default data collator that will just convert everything
+        # to tensors.
+        data_collator = default_data_collator
+    else:
+        # Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of
+        # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
+        # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
+        data_collator = DataCollatorForTokenClassification(
+            tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)
+        )
+
+    train_dataloader = DataLoader(
+        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
+    )
+    eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
+
+    # Optimizer
+    # Split weights in two groups, one with weight decay and the other not.
+    no_decay = ["bias", "LayerNorm.weight"]
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+            "weight_decay": args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
+
+    # Use the device given by the `accelerator` object.
+    device = accelerator.device
+    model.to(device)
+
+    # Prepare everything with our `accelerator`.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )
+
+    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
+    # shorter in multiprocess)
+
+    # Scheduler and math around the number of training steps.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    else:
+        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    lr_scheduler = get_scheduler(
+        name=args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=args.num_warmup_steps,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # Metrics
+    metric = load_metric("seqeval")
+
+    def get_labels(predictions, references):
+        # Transform predictions and references tensos to numpy arrays
+        if device.type == "cpu":
+            y_pred = predictions.detach().clone().numpy()
+            y_true = references.detach().clone().numpy()
+        else:
+            y_pred = predictions.detach().cpu().clone().numpy()
+            y_true = references.detach().cpu().clone().numpy()
+
+        # Remove ignored index (special tokens)
+        true_predictions = [
+            [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100]
+            for pred, gold_label in zip(y_pred, y_true)
+        ]
+        true_labels = [
+            [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100]
+            for pred, gold_label in zip(y_pred, y_true)
+        ]
+        return true_predictions, true_labels
+
+    def compute_metrics():
+        results = metric.compute()
+        if 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"],
+            }
+
+    # Train!
+    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+    completed_steps = 0
+
+    for epoch in range(args.num_train_epochs):
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss = loss / args.gradient_accumulation_steps
+            accelerator.backward(loss)
+            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+                progress_bar.update(1)
+                completed_steps += 1
+
+            if completed_steps >= args.max_train_steps:
+                break
+
+        model.eval()
+        for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad():
+                outputs = model(**batch)
+            predictions = outputs.logits.argmax(dim=-1)
+            labels = batch["labels"]
+            if not args.pad_to_max_length:  # necessary to pad predictions and labels for being gathered
+                predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)
+                labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
+
+            predictions_gathered = accelerator.gather(predictions)
+            labels_gathered = accelerator.gather(labels)
+            preds, refs = get_labels(predictions_gathered, labels_gathered)
+            metric.add_batch(
+                predictions=preds,
+                references=refs,
+            )  # predictions and preferences are expected to be a nested list of labels, not label_ids
+            preds, refs = get_labels(predictions_gathered, labels_gathered)
+            metric.add_batch(
+                predictions=preds,
+                references=refs,
+            )  # predictions and preferences are expected to be a nested list
+
+        # eval_metric = metric.compute()
+        eval_metric = compute_metrics()
+        accelerator.print(f"epoch {epoch}:", eval_metric)
+
+    if args.output_dir is not None:
+        accelerator.wait_for_everyone()
+        unwrapped_model = accelerator.unwrap_model(model)
+        unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+
+
+if __name__ == "__main__":
+    main()

examples/token-classification/run_no_trainer.sh

@@ -0,0 +1,21 @@
+# 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.
+
+accelerate launch run_ner_no_trainer.py \
+  --model_name_or_path bert-base-uncased \
+  --dataset_name conll2003 \
+  --output_dir /tmp/test-ner \
+  --pad_to_max_length \
+  --task_name ner \
+  --return_entity_level_metrics