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# distilbert_classifier_newsgroups

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [20Newsgroups](http://qwone.com/~jason/20Newsgroups/) dataset.
It achieves the following results on the evaluation set:


## Model description

We have fine-tuned the distilbert-base-uncased to classify news in 20 main topics based on the labeled dataset [20Newsgroups](http://qwone.com/~jason/20Newsgroups/).



## Training and evaluation data

The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and 
the other one for testing (or for performance evaluation). 
The split between the train and test set is based upon a messages posted before and after a specific date.

These are the 20 topics we fine-tuned the model on:

 'alt.atheism',
 'comp.graphics',
 'comp.os.ms-windows.misc',
 'comp.sys.ibm.pc.hardware',
 'comp.sys.mac.hardware',
 'comp.windows.x',
 'misc.forsale',
 'rec.autos',
 'rec.motorcycles',
 'rec.sport.baseball',
 'rec.sport.hockey',
 'sci.crypt',
 'sci.electronics',
 'sci.med',
 'sci.space',
 'soc.religion.christian',
 'talk.politics.guns',
 'talk.politics.mideast',
 'talk.politics.misc',
 'talk.religion.misc'


### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results
Epoch 1/3
637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304
Epoch 2/3
637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799
Epoch 3/3
637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993
CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s
Wall time: 3min 15s
<keras.callbacks.History at 0x7f481afbfbb0>


### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "pipeline_tag": "text-classification", "model-index": [{"name": "distilbert_classifier_newsgroups", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# distilbert_classifier_newsgroups

This model is a fine-tuned version of distilbert-base-uncased on 20Newsgroups dataset.
It achieves the following results on the evaluation set:


## Model description

We have fine-tuned the distilbert-base-uncased to classify news in 20 main topics based on the labeled dataset 20Newsgroups.



## Training and evaluation data

The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and 
the other one for testing (or for performance evaluation). 
The split between the train and test set is based upon a messages posted before and after a specific date.

These are the 20 topics we fine-tuned the model on:

 'alt.atheism',
 'comp.graphics',
 'URL',
 'URL.hardware',
 'URL.hardware',
 'comp.windows.x',
 'misc.forsale',
 'URL',
 'rec.motorcycles',
 'URL.baseball',
 'URL',
 'URL',
 'sci.electronics',
 'URL',
 'URL',
 'soc.religion.christian',
 'URL',
 'talk.politics.mideast',
 'URL',
 'URL'


### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results
Epoch 1/3
637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304
Epoch 2/3
637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799
Epoch 3/3
637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993
CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s
Wall time: 3min 15s
<keras.callbacks.History at 0x7f481afbfbb0>


### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
# distilbert_classifier_newsgroups

This model is a fine-tuned version of distilbert-base-uncased on 20Newsgroups dataset.
It achieves the following results on the evaluation set: ## Model description

We have fine-tuned the distilbert-base-uncased to classify news in 20 main topics based on the labeled dataset 20Newsgroups. ## Training and evaluation data

The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and 
the other one for testing (or for performance evaluation). 
The split between the train and test set is based upon a messages posted before and after a specific date.

These are the 20 topics we fine-tuned the model on:

 'alt.atheism',
 'comp.graphics',
 'URL',
 'URL.hardware',
 'URL.hardware',
 'comp.windows.x',
 'misc.forsale',
 'URL',
 'rec.motorcycles',
 'URL.baseball',
 'URL',
 'URL',
 'sci.electronics',
 'URL',
 'URL',
 'soc.religion.christian',
 'URL',
 'talk.politics.mideast',
 'URL',
 'URL' ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32 ### Training results
Epoch 1/3
637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304
Epoch 2/3
637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799
Epoch 3/3
637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993
CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s
Wall time: 3min 15s
<keras.callbacks.History at 0x7f481afbfbb0> ### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert_classifier_newsgroups

This model is a fine-tuned version of distilbert-base-uncased on 20Newsgroups dataset.
It achieves the following results on the evaluation set: ## Model description

We have fine-tuned the distilbert-base-uncased to classify news in 20 main topics based on the labeled dataset 20Newsgroups. ## Training and evaluation data

The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and 
the other one for testing (or for performance evaluation). 
The split between the train and test set is based upon a messages posted before and after a specific date.

These are the 20 topics we fine-tuned the model on:

 'alt.atheism',
 'comp.graphics',
 'URL',
 'URL.hardware',
 'URL.hardware',
 'comp.windows.x',
 'misc.forsale',
 'URL',
 'rec.motorcycles',
 'URL.baseball',
 'URL',
 'URL',
 'sci.electronics',
 'URL',
 'URL',
 'soc.religion.christian',
 'URL',
 'talk.politics.mideast',
 'URL',
 'URL' ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32 ### Training results
Epoch 1/3
637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304
Epoch 2/3
637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799
Epoch 3/3
637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993
CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s
Wall time: 3min 15s
<keras.callbacks.History at 0x7f481afbfbb0> ### Framework versions

- Transformers 4.28.0
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3