uk-rs/CbertActiveLearning_initial_V_1.0

README.md

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+---
+library_name: transformers
+base_model: medicalai/ClinicalBERT
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: results
+  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. -->
+
+# results
+
+This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8614
+- Accuracy: 0.6145
+- Precision: 0.6243
+- Recall: 0.6145
+- F1: 0.5971
+- Roc Auc: 0.8073
+
+## 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: 8
+- eval_batch_size: 8
+- 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     | Roc Auc |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| No log        | 1.0   | 42   | 1.0433          | 0.4458   | 0.4351    | 0.4458 | 0.3685 | 0.7162  |
+| No log        | 2.0   | 84   | 0.8946          | 0.5663   | 0.5641    | 0.5663 | 0.5559 | 0.7823  |
+| No log        | 3.0   | 126  | 0.9142          | 0.5783   | 0.6385    | 0.5783 | 0.5332 | 0.7896  |
+| No log        | 4.0   | 168  | 0.8497          | 0.6386   | 0.6434    | 0.6386 | 0.6299 | 0.8084  |
+| No log        | 5.0   | 210  | 0.8614          | 0.6145   | 0.6243    | 0.6145 | 0.5971 | 0.8073  |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 2.14.5
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "medicalai/ClinicalBERT",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "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": "4.46.3",
+  "vocab_size": 119547
+}

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