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Training in progress epoch 0

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Files changed (7) hide show
  1. README.md +58 -0
  2. config.json +29 -0
  3. special_tokens_map.json +7 -0
  4. tf_model.h5 +3 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +55 -0
  7. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/electra-small-discriminator
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+ tags:
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+ - generated_from_keras_callback
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+ model-index:
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+ - name: nguyennghia0902/electra-small-discriminator_0.0005_16
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+ results: []
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+ ---
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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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+
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+ # nguyennghia0902/electra-small-discriminator_0.0005_16
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+
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+ This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Train Loss: 3.8593
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+ - Train End Logits Accuracy: 0.1838
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+ - Train Start Logits Accuracy: 0.1690
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+ - Validation Loss: 2.9074
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+ - Validation End Logits Accuracy: 0.3506
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+ - Validation Start Logits Accuracy: 0.3319
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+ - Epoch: 0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 31270, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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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+
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+ ### Training results
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+
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+ | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
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+ |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
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+ | 3.8593 | 0.1838 | 0.1690 | 2.9074 | 0.3506 | 0.3319 | 0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - TensorFlow 2.15.0
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
config.json ADDED
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+ {
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+ "_name_or_path": "google/electra-small-discriminator",
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+ "architectures": [
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+ "ElectraForQuestionAnswering"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "embedding_size": 128,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 256,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1024,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "electra",
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+ "num_attention_heads": 4,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "summary_activation": "gelu",
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+ "summary_last_dropout": 0.1,
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+ "summary_type": "first",
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+ "summary_use_proj": true,
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+ "transformers_version": "4.39.3",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
special_tokens_map.json ADDED
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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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+ }
tf_model.h5 ADDED
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+ size 54221088
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {
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+ "added_tokens_decoder": {
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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": "ElectraTokenizer",
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+ "unk_token": "[UNK]"
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+ }
vocab.txt ADDED
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