Salm00n/bert-base-uncased-Federal-Regulations

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: bert-base-uncased-Federal-Regulations
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-base-uncased-Federal-Regulations
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7175
+- Accuracy: 0.7394
+- Precision: 0.7542
+- Recall: 0.7394
+- F1: 0.7447
+- Confusion Matrix: [[2626, 759], [504, 957]]
+
+## 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:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Confusion Matrix            |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---------------------------:|
+| 0.5834        | 1.0   | 600  | 0.5950          | 0.6771   | 0.7393    | 0.6771 | 0.6901 | [[2201, 1184], [381, 1080]] |
+| 0.5163        | 2.0   | 1200 | 0.5336          | 0.7528   | 0.7531    | 0.7528 | 0.7529 | [[2783, 602], [596, 865]]   |
+| 0.4601        | 3.0   | 1800 | 0.5809          | 0.7350   | 0.7562    | 0.7350 | 0.7419 | [[2570, 815], [469, 992]]   |
+| 0.4002        | 4.0   | 2400 | 0.6231          | 0.7524   | 0.7570    | 0.7524 | 0.7544 | [[2740, 645], [555, 906]]   |
+| 0.3001        | 5.0   | 3000 | 0.7175          | 0.7394   | 0.7542    | 0.7394 | 0.7447 | [[2626, 759], [504, 957]]   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "NEGATIVE",
+    "1": "POSITIVE"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "NEGATIVE": 0,
+    "POSITIVE": 1
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "vocab_size": 30522
+}

model.safetensors

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runs/Dec04_13-37-41_0422f23f8a7c/events.out.tfevents.1733319481.0422f23f8a7c.276.11

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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]"
+}

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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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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