Training in progress epoch 0

README.md

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+---
+license: mit
+base_model: prajjwal1/bert-tiny
+tags:
+- generated_from_keras_callback
+model-index:
+- name: LeoZZzzZZ/bert-tiny-finetuned-fact
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# LeoZZzzZZ/bert-tiny-finetuned-fact
+
+This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 1.0374
+- Validation Loss: 0.9560
+- Train Accuracy: 0.5360
+- Train Precision: 0.4193
+- Train Recall: 0.5360
+- Train F1: 0.4643
+- Epoch: 0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11870, '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}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
+|:----------:|:---------------:|:--------------:|:---------------:|:------------:|:--------:|:-----:|
+| 1.0374     | 0.9560          | 0.5360         | 0.4193          | 0.5360       | 0.4643   | 0     |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- TensorFlow 2.15.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "prajjwal1/bert-tiny",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "NOT ENOUGH INFO",
+    "1": "SUPPORTS",
+    "2": "REFUTES"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "NOT ENOUGH INFO": 0,
+    "REFUTES": 2,
+    "SUPPORTS": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "transformers_version": "4.40.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

logs/train/events.out.tfevents.1716095701.6264913a9602.740.5.v2

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logs/validation/events.out.tfevents.1716095738.6264913a9602.740.6.v2

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tf_model.h5

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+size 17614080

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "[UNK]",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}