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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: distilbert_classifier_newsgroups |
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results: [] |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# distilbert_classifier_newsgroups |
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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. |
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It achieves the following results on the evaluation set: |
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## Model description |
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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/). |
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## Training and evaluation data |
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The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and |
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the other one for testing (or for performance evaluation). |
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The split between the train and test set is based upon a messages posted before and after a specific date. |
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These are the 20 topics we fine-tuned the model on: |
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'alt.atheism', |
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'comp.graphics', |
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'comp.os.ms-windows.misc', |
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'comp.sys.ibm.pc.hardware', |
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'comp.sys.mac.hardware', |
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'comp.windows.x', |
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'misc.forsale', |
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'rec.autos', |
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'rec.motorcycles', |
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'rec.sport.baseball', |
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'rec.sport.hockey', |
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'sci.crypt', |
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'sci.electronics', |
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'sci.med', |
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'sci.space', |
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'soc.religion.christian', |
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'talk.politics.guns', |
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'talk.politics.mideast', |
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'talk.politics.misc', |
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'talk.religion.misc' |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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Epoch 1/3 |
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637/637 [==============================] - 110s 131ms/step - loss: 1.3480 - accuracy: 0.6633 - val_loss: 0.6122 - val_accuracy: 0.8304 |
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Epoch 2/3 |
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637/637 [==============================] - 44s 70ms/step - loss: 0.4498 - accuracy: 0.8812 - val_loss: 0.4342 - val_accuracy: 0.8799 |
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Epoch 3/3 |
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637/637 [==============================] - 40s 64ms/step - loss: 0.2685 - accuracy: 0.9355 - val_loss: 0.3756 - val_accuracy: 0.8993 |
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CPU times: user 3min 4s, sys: 8.76 s, total: 3min 13s |
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Wall time: 3min 15s |
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<keras.callbacks.History at 0x7f481afbfbb0> |
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### Framework versions |
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- Transformers 4.28.0 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
