sakren/distil-bert-imeocap

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: distil-bert-imeocap
+  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. -->
+
+# distil-bert-imeocap
+
+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: 1.8186
+- F1: 0.6341
+- Precision: 0.6365
+- Recall: 0.6365
+- Accuracy: 0.6365
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
+| 0.1961        | 1.0   | 74   | 1.6080          | 0.6314 | 0.6285    | 0.6385 | 0.6385   |
+| 0.1845        | 2.0   | 148  | 1.7125          | 0.6298 | 0.6317    | 0.6385 | 0.6385   |
+| 0.1717        | 3.0   | 222  | 1.9402          | 0.6226 | 0.6364    | 0.6385 | 0.6385   |
+| 0.176         | 4.0   | 296  | 1.8028          | 0.6169 | 0.6253    | 0.6192 | 0.6192   |
+| 0.1679        | 5.0   | 370  | 1.6948          | 0.6243 | 0.6285    | 0.625  | 0.625    |
+| 0.168         | 6.0   | 444  | 1.8304          | 0.6317 | 0.6336    | 0.6385 | 0.6385   |
+| 0.1617        | 7.0   | 518  | 1.7457          | 0.6286 | 0.6310    | 0.6308 | 0.6308   |
+| 0.1677        | 8.0   | 592  | 1.8071          | 0.6422 | 0.6382    | 0.65   | 0.65     |
+| 0.171         | 9.0   | 666  | 1.8177          | 0.6323 | 0.6326    | 0.6385 | 0.6385   |
+| 0.1683        | 10.0  | 740  | 1.8265          | 0.6347 | 0.6370    | 0.6365 | 0.6365   |
+| 0.1808        | 11.0  | 814  | 1.7734          | 0.6304 | 0.6365    | 0.6308 | 0.6308   |
+| 0.1757        | 12.0  | 888  | 1.7727          | 0.6244 | 0.6296    | 0.6231 | 0.6231   |
+| 0.1897        | 13.0  | 962  | 1.8449          | 0.6374 | 0.6377    | 0.6404 | 0.6404   |
+| 0.1674        | 14.0  | 1036 | 1.8244          | 0.6455 | 0.6462    | 0.6481 | 0.6481   |
+| 0.1746        | 15.0  | 1110 | 1.8186          | 0.6341 | 0.6365    | 0.6365 | 0.6365   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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