Training in progress epoch 0

README.md

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+---
+license: cc-by-sa-4.0
+base_model: nlpaueb/legal-bert-base-uncased
+tags:
+- generated_from_keras_callback
+model-index:
+- name: Sidziesama/Legal_NER_Support_Model
+  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. -->
+
+# Sidziesama/Legal_NER_Support_Model
+
+This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 0.3771
+- Validation Loss: 0.1228
+- Train Precision: 0.8400
+- Train Recall: 0.8644
+- Train F1: 0.8520
+- Train Accuracy: 0.9655
+- 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2945, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
+|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
+| 0.3771     | 0.1228          | 0.8400          | 0.8644       | 0.8520   | 0.9655         | 0     |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- TensorFlow 2.15.0
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "nlpaueb/legal-bert-base-uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-CASE_NUMBER",
+    "1": "B-COURT",
+    "2": "B-DATE",
+    "3": "B-GPE",
+    "4": "B-JUDGE",
+    "5": "B-ORG",
+    "6": "B-OTHER_PERSON",
+    "7": "B-PETITIONER",
+    "8": "B-PRECEDENT",
+    "9": "B-PROVISION",
+    "10": "B-RESPONDENT",
+    "11": "B-STATUTE",
+    "12": "B-WITNESS",
+    "13": "I-CASE_NUMBER",
+    "14": "I-COURT",
+    "15": "I-DATE",
+    "16": "I-GPE",
+    "17": "I-JUDGE",
+    "18": "I-ORG",
+    "19": "I-OTHER_PERSON",
+    "20": "I-PETITIONER",
+    "21": "I-PRECEDENT",
+    "22": "I-PROVISION",
+    "23": "I-RESPONDENT",
+    "24": "I-STATUTE",
+    "25": "I-WITNESS",
+    "26": "O"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-CASE_NUMBER": 0,
+    "B-COURT": 1,
+    "B-DATE": 2,
+    "B-GPE": 3,
+    "B-JUDGE": 4,
+    "B-ORG": 5,
+    "B-OTHER_PERSON": 6,
+    "B-PETITIONER": 7,
+    "B-PRECEDENT": 8,
+    "B-PROVISION": 9,
+    "B-RESPONDENT": 10,
+    "B-STATUTE": 11,
+    "B-WITNESS": 12,
+    "I-CASE_NUMBER": 13,
+    "I-COURT": 14,
+    "I-DATE": 15,
+    "I-GPE": 16,
+    "I-JUDGE": 17,
+    "I-ORG": 18,
+    "I-OTHER_PERSON": 19,
+    "I-PETITIONER": 20,
+    "I-PRECEDENT": 21,
+    "I-PROVISION": 22,
+    "I-RESPONDENT": 23,
+    "I-STATUTE": 24,
+    "I-WITNESS": 25,
+    "O": 26
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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 435935284

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}