source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#evaluate | .md | ... results = seqeval.compute(predictions=true_predictions, references=true_labels)
... return {
... "precision": results["overall_precision"],
... "recall": results["overall_recall"],
... "f1": results["overall_f1"],
... "accuracy": results["overall_accuracy"],
... }
```
Y... | 82_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:
```py
>>> id2label = {
... 0: "O",
... 1: "B-corporation",
... 2: "I-corporation",
... 3: "B-creative-work",
... 4: "I-creative-work",
... 5: "B-group",
... 6: "I-group",
... | 82_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ... 10: "I-person",
... 11: "B-product",
... 12: "I-product",
... }
>>> label2id = {
... "O": 0,
... "B-corporation": 1,
... "I-corporation": 2,
... "B-creative-work": 3,
... "I-creative-work": 4,
... "B-group": 5,
... "I-group": 6,
... "B-location": 7,
... "I-location": ... | 82_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ... "I-person": 10,
... "B-product": 11,
... "I-product": 12,
... }
```
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your mode... | 82_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ```py
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer | 82_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> model = AutoModelForTokenClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
At this point, only three steps remain: | 82_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to uploa... | 82_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_wnut_model",
... learning_rate=2e-5,
... per_dev... | 82_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ... num_train_epochs=2,
... weight_decay=0.01,
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,
... ) | 82_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_wnut["train"],
... eval_dataset=tokenized_wnut["test"],
... processing_class=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... ) | 82_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [h... | 82_5_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | </Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer | 82_5_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> batch_size = 16
>>> num_train_epochs = 3
>>> num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=2e-5,
... num_train_steps=num_train_steps,
... weight_decay_rate=0.01,
... num_warmup_steps=0,
... )
```
Then yo... | 82_5_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ```py
>>> from transformers import TFAutoModelForTokenClassification | 82_5_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> model = TFAutoModelForTokenClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model... | 82_5_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_wnut["validation"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers m... | 82_5_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> model.compile(optimizer=optimizer) # No loss argument!
```
The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute... | 82_5_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback | 82_5_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | >>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_wnut_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/... | 82_5_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | ```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for token classificat... | 82_5_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | .md | or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).
</Tip> | 82_5_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:
```py
>>> text = "The Golden State Warriors are an American professional basketball team based in San Francisco."
```
The simplest way to try out your finetuned model for inference is to use it i... | 82_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model")
>>> classifier(text)
[{'entity': 'B-location',
'score': 0.42658573,
'index': 2,
'word': 'golden',
'start': 4,
'end': 10},
{'entity': 'I-location',
'score': 0.35856336,
'index': 3,
'word': 'state',
'start': 11,
'end': 16},
{'entity': 'B-group',
'sc... | 82_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | 'start': 17,
'end': 25},
{'entity': 'B-location',
'score': 0.65523505,
'index': 13,
'word': 'san',
'start': 80,
'end': 83},
{'entity': 'B-location',
'score': 0.4668663,
'index': 14,
'word': 'francisco',
'start': 84,
'end': 93}]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<framew... | 82_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="pt")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import AutoModelForTokenClassification | 82_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predictions = torch.argmax(l... | 82_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</pt>
<tf>
Tokenize the text and return TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer | 82_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="tf")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForTokenClassification | 82_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=... | 82_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | .md | >>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</tf>
</frameworkcontent> | 82_6_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/ | .md | <!--Copyright 2022 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 agr... | 83_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/ | .md | 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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 83_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#text-classification | .md | [[open-in-colab]]
<Youtube id="leNG9fN9FQU"/>
Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, wh... | 83_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#text-classification | .md | This guide will show you how to:
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
2. Use your finetuned model for inference.
<Tip>
To see all architectures an... | 83_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#text-classification | .md | </Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate accelerate
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>... | 83_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#text-classification | .md | >>> notebook_login()
``` | 83_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | Start by loading the IMDb dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset | 83_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | >>> imdb = load_dataset("imdb")
```
Then take a look at an example:
```py
>>> imdb["test"][0]
{
"label": 0, | 83_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | "text": "I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that does... | 83_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat import... | 83_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth...\" otherwise people would not continue watching. R... | 83_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.", | 83_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | .md | }
```
There are two fields in this dataset:
- `text`: the movie review text.
- `label`: a value that is either `0` for a negative review or `1` for a positive review. | 83_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#preprocess | .md | The next step is to load a DistilBERT tokenizer to preprocess the `text` field:
```py
>>> from transformers import AutoTokenizer | 83_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#preprocess | .md | >>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
Create a preprocessing function to tokenize `text` and truncate sequences to be no longer than DistilBERT's maximum input length:
```py
>>> def preprocess_function(examples):
... return tokenizer(examples["text"], truncation... | 83_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#preprocess | .md | ```py
>>> def preprocess_function(examples):
... return tokenizer(examples["text"], truncation=True)
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up `map` by setting `batched=True` to process multiple elements of the dataset at ... | 83_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#preprocess | .md | ```py
tokenized_imdb = imdb.map(preprocess_function, batched=True)
```
Now create a batch of examples using [`DataCollatorWithPadding`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
<frameworkcon... | 83_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#preprocess | .md | >>> data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorWithPadding
>>> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
```
</tf>
</frameworkcontent> | 83_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#evaluate | .md | Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 ... | 83_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#evaluate | .md | >>> accuracy = evaluate.load("accuracy")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the accuracy:
```py
>>> import numpy as np | 83_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#evaluate | .md | >>> def compute_metrics(eval_pred):
... predictions, labels = eval_pred
... predictions = np.argmax(predictions, axis=1)
... return accuracy.compute(predictions=predictions, references=labels)
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training. | 83_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:
```py
>>> id2label = {0: "NEGATIVE", 1: "POSITIVE"}
>>> label2id = {"NEGATIVE": 0, "POSITIVE": 1}
```
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Train... | 83_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | </Tip>
You're ready to start training your model now! Load DistilBERT with [`AutoModelForSequenceClassification`] along with the number of expected labels, and the label mappings:
```py
>>> from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer | 83_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> model = AutoModelForSequenceClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )
```
At this point, only three steps remain: | 83_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | ... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to uploa... | 83_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_model",
... learning_rate=2e-5,
... per_device_t... | 83_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | ... num_train_epochs=2,
... weight_decay=0.01,
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,
... ) | 83_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_imdb["train"],
... eval_dataset=tokenized_imdb["test"],
... processing_class=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... ) | 83_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> trainer.train()
```
<Tip>
[`Trainer`] applies dynamic padding by default when you pass `tokenizer` to it. In this case, you don't need to specify a data collator explicitly.
</Tip>
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can u... | 83_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | ```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and ... | 83_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> batch_size = 16
>>> num_epochs = 5
>>> batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
>>> total_train_steps = int(batches_per_epoch * num_epochs)
>>> optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
```
Then you can load DistilBERT with [`... | 83_5_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> model = TFAutoModelForSequenceClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = mod... | 83_5_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_imdb["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models ... | 83_5_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> model.compile(optimizer=optimizer) # No loss argument!
```
The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).
Pass your `compute_metri... | 83_5_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback | 83_5_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | >>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_model",
... tokenizer=tokenizer,
... )
```
Then bundle your callbacks together:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/m... | 83_5_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | ```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for text classificati... | 83_5_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#train | .md | [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).
</Tip> | 83_5_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:
```py
>>> text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
```
The simplest way to try out your fin... | 83_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | ```py
>>> from transformers import pipeline | 83_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | >>> classifier = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model")
>>> classifier(text)
[{'label': 'POSITIVE', 'score': 0.9994940757751465}]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Tokenize the text and return PyTorch tensors:
```py
... | 83_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | >>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
>>> inputs = tokenizer(text, return_tensors="pt")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import AutoModelForSequenceClassification | 83_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | >>> model = AutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predicted_class_id = logits.ar... | 83_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | >>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_model")
>>> inputs = tokenizer(text, return_tensors="tf")
```
Pass your inputs to the model and return the `logits`:
```py
>>> from transformers import TFAutoModelForSequenceClassification | 83_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#inference | .md | >>> model = TFAutoModelForSequenceClassification.from_pretrained("stevhliu/my_awesome_model")
>>> logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:
```py
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0... | 83_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/ | .md | <!--Copyright 2023 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 agr... | 84_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/ | .md | 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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 84_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | [[open-in-colab]] | 84_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | Knowledge distillation is a technique used to transfer knowledge from a larger, more complex model (teacher) to a smaller, simpler model (student). To distill knowledge from one model to another, we take a pre-trained teacher model trained on a certain task (image classification for this case) and randomly initialize a... | 84_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | we train the student model to minimize the difference between its outputs and the teacher's outputs, thus making it mimic the behavior. It was first introduced in [Distilling the Knowledge in a Neural Network by Hinton et al](https://arxiv.org/abs/1503.02531). In this guide, we will do task-specific knowledge distillat... | 84_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | This guide demonstrates how you can distill a [fine-tuned ViT model](https://huggingface.co/merve/vit-mobilenet-beans-224) (teacher model) to a [MobileNet](https://huggingface.co/google/mobilenet_v2_1.4_224) (student model) using the [TrainerAPI](https://huggingface.co/docs/transformers/en/main_classes/trainer#trainer)... | 84_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | ```bash
pip install transformers datasets accelerate tensorboard evaluate --upgrade
```
In this example, we are using the `merve/beans-vit-224` model as teacher model. It's an image classification model, based on `google/vit-base-patch16-224-in21k` fine-tuned on beans dataset. We will distill this model to a randomly... | 84_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | dataset = load_dataset("beans")
```
We can use an image processor from either of the models, as in this case they return the same output with same resolution. We will use the `map()` method of `dataset` to apply the preprocessing to every split of the dataset.
```python
from transformers import AutoImageProcessor
t... | 84_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | processed_datasets = dataset.map(process, batched=True)
``` | 84_1_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | Essentially, we want the student model (a randomly initialized MobileNet) to mimic the teacher model (fine-tuned vision transformer). To achieve this, we first get the logits output from the teacher and the student. Then, we divide each of them by the parameter `temperature` which controls the importance of each soft t... | 84_1_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | distillation loss. In this example, we will use `temperature=5` and `lambda=0.5`. We will use the Kullback-Leibler Divergence loss to compute the divergence between the student and teacher. Given two data P and Q, KL Divergence explains how much extra information we need to represent P using Q. If two are identical, th... | 84_1_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | ```python
from transformers import TrainingArguments, Trainer
import torch
import torch.nn as nn
import torch.nn.functional as F
from accelerate.test_utils.testing import get_backend | 84_1_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | class ImageDistilTrainer(Trainer):
def __init__(self, teacher_model=None, student_model=None, temperature=None, lambda_param=None, *args, **kwargs):
super().__init__(model=student_model, *args, **kwargs)
self.teacher = teacher_model
self.student = student_model
self.loss_function = nn.KLDivLoss(reduction="batchmean")
... | 84_1_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | self.teacher.to(device)
self.teacher.eval()
self.temperature = temperature
self.lambda_param = lambda_param | 84_1_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | def compute_loss(self, student, inputs, return_outputs=False):
student_output = self.student(**inputs)
with torch.no_grad():
teacher_output = self.teacher(**inputs)
# Compute soft targets for teacher and student
soft_teacher = F.softmax(teacher_output.logits / self.temperature, dim=-1)
soft_student = F.log_softmax(st... | 84_1_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | # Compute the loss
distillation_loss = self.loss_function(soft_student, soft_teacher) * (self.temperature ** 2)
# Compute the true label loss
student_target_loss = student_output.loss | 84_1_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | # Compute the true label loss
student_target_loss = student_output.loss
# Calculate final loss
loss = (1. - self.lambda_param) * student_target_loss + self.lambda_param * distillation_loss
return (loss, student_output) if return_outputs else loss
```
We will now login to Hugging Face Hub so we can push our model to ... | 84_1_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | notebook_login()
```
Let's set the `TrainingArguments`, the teacher model and the student model.
```python
from transformers import AutoModelForImageClassification, MobileNetV2Config, MobileNetV2ForImageClassification | 84_1_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | training_args = TrainingArguments(
output_dir="my-awesome-model",
num_train_epochs=30,
fp16=True,
logging_dir=f"{repo_name}/logs",
logging_strategy="epoch",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
report_to="tensorboard",
push_to_hub=True,
hub_strateg... | 84_1_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | num_labels = len(processed_datasets["train"].features["labels"].names)
# initialize models
teacher_model = AutoModelForImageClassification.from_pretrained(
"merve/beans-vit-224",
num_labels=num_labels,
ignore_mismatched_sizes=True
) | 84_1_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | # training MobileNetV2 from scratch
student_config = MobileNetV2Config()
student_config.num_labels = num_labels
student_model = MobileNetV2ForImageClassification(student_config)
```
We can use `compute_metrics` function to evaluate our model on the test set. This function will be used during the training process to c... | 84_1_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | accuracy = evaluate.load("accuracy")
def compute_metrics(eval_pred):
predictions, labels = eval_pred
acc = accuracy.compute(references=labels, predictions=np.argmax(predictions, axis=1))
return {"accuracy": acc["accuracy"]}
```
Let's initialize the `Trainer` with the training arguments we defined. We will also initi... | 84_1_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | data_collator = DefaultDataCollator()
trainer = ImageDistilTrainer(
student_model=student_model,
teacher_model=teacher_model,
training_args=training_args,
train_dataset=processed_datasets["train"],
eval_dataset=processed_datasets["validation"],
data_collator=data_collator,
processing_class=teacher_processor,
compute_me... | 84_1_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | )
```
We can now train our model.
```python
trainer.train()
```
We can evaluate the model on the test set.
```python
trainer.evaluate(processed_datasets["test"])
``` | 84_1_21 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md | https://huggingface.co/docs/transformers/en/tasks/knowledge_distillation_for_image_classification/#knowledge-distillation-for-computer-vision | .md | On test set, our model reaches 72 percent accuracy. To have a sanity check over efficiency of distillation, we also trained MobileNet on the beans dataset from scratch with the same hyperparameters and observed 63 percent accuracy on the test set. We invite the readers to try different pre-trained teacher models, stude... | 84_1_22 |
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