ClinicalBERT Versione dopo 13 epochs

README.md

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+---
+base_model: medicalai/ClinicalBERT
+tags:
+- generated_from_trainer
+model-index:
+- name: ICU_Returns_ClinicalBERT
+  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. -->
+
+# ICU_Returns_ClinicalBERT
+
+This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3201
+- F1:: 0.7134
+- Roc Auc: 0.7225
+- Precision with 0:: 0.8462
+- Precision with 1:: 0.6640
+- Recall with 0:: 0.5440
+- Recal  with 1:: 0.9011
+- Accuracy:: 0.7225
+
+## 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: 0.0001
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 13
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1:    | Roc Auc | Precision with 0: | Precision with 1: | Recall with 0: | Recal  with 1: | Accuracy: |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:---------:|
+| No log        | 1.0   | 46   | 0.7057          | 0.3454 | 0.5055  | 1.0               | 0.5028            | 0.0110         | 1.0            | 0.5055    |
+| No log        | 2.0   | 92   | 0.6827          | 0.5715 | 0.5742  | 0.5882            | 0.5640            | 0.4945         | 0.6538         | 0.5742    |
+| No log        | 3.0   | 138  | 0.7221          | 0.4612 | 0.5467  | 0.7297            | 0.5260            | 0.1484         | 0.9451         | 0.5467    |
+| No log        | 4.0   | 184  | 0.6284          | 0.6693 | 0.6841  | 0.6293            | 0.8190            | 0.8956         | 0.4725         | 0.6841    |
+| No log        | 5.0   | 230  | 0.9235          | 0.6283 | 0.6401  | 0.7179            | 0.6032            | 0.4615         | 0.8187         | 0.6401    |
+| No log        | 6.0   | 276  | 0.8772          | 0.6534 | 0.6648  | 0.7586            | 0.6210            | 0.4835         | 0.8462         | 0.6648    |
+| No log        | 7.0   | 322  | 0.7968          | 0.7677 | 0.7692  | 0.8224            | 0.7311            | 0.6868         | 0.8516         | 0.7692    |
+| No log        | 8.0   | 368  | 0.6826          | 0.8132 | 0.8132  | 0.8167            | 0.8098            | 0.8077         | 0.8187         | 0.8132    |
+| No log        | 9.0   | 414  | 1.2195          | 0.6950 | 0.7033  | 0.8033            | 0.6529            | 0.5385         | 0.8681         | 0.7033    |
+| No log        | 10.0  | 460  | 0.9542          | 0.7617 | 0.7637  | 0.8243            | 0.7222            | 0.6703         | 0.8571         | 0.7637    |
+| 0.3635        | 11.0  | 506  | 1.3032          | 0.7079 | 0.7143  | 0.8047            | 0.6653            | 0.5659         | 0.8626         | 0.7143    |
+| 0.3635        | 12.0  | 552  | 1.4170          | 0.7063 | 0.7143  | 0.8197            | 0.6612            | 0.5495         | 0.8791         | 0.7143    |
+| 0.3635        | 13.0  | 598  | 1.3201          | 0.7134 | 0.7225  | 0.8462            | 0.6640            | 0.5440         | 0.9011         | 0.7225    |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.14.5
+- Tokenizers 0.14.1

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,
+  "initializer_range": 0.02,
+  "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": "4.34.0",
+  "vocab_size": 119547
+}

pytorch_model.bin

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training_args.bin

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