semihGuner2002
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Commit
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Training in progress epoch 0
Browse files- README.md +54 -0
- config.json +31 -0
- logs/train/events.out.tfevents.1704799615.6e3b7e342285.1919.2.v2 +3 -0
- logs/validation/events.out.tfevents.1704799970.6e3b7e342285.1919.3.v2 +3 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +55 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_keras_callback
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model-index:
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- name: semihGuner2002/distilbert-base-uncased-finetuned-URL
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results: []
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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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# semihGuner2002/distilbert-base-uncased-finetuned-URL
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0000
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- Validation Loss: 0.0
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- Epoch: 0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Epoch |
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|:----------:|:---------------:|:-----:|
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| 0.0000 | 0.0 | 0 |
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### Framework versions
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- Transformers 4.35.2
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- TensorFlow 2.15.0
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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config.json
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "Malignant",
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"1": "Benign"
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},
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"initializer_range": 0.02,
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"label2id": {
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"Benign": 1,
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"Malignant": 0
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.35.2",
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"vocab_size": 30522
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}
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logs/train/events.out.tfevents.1704799615.6e3b7e342285.1919.2.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:75c0a1656f9fed1fd44cc527c5752c9a94476c88da48ba58fd5d9bdb88ab6317
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size 1921082
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logs/validation/events.out.tfevents.1704799970.6e3b7e342285.1919.3.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:0a255d9052f99707dbbe287e624f6496848006b7ee6e60b197a8920df9a9295e
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size 232
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:63d7a122c01fc25ce13a89e25044876acf7cce85078be090807b6cc8dfaf5918
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size 267955144
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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