sakren/debert-imeocap

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: debert-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. -->
+
+# debert-imeocap
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3914
+- F1: 0.6372
+- Precision: 0.6448
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
+| 1.5405        | 1.0   | 74   | 1.4488          | 0.3206 | 0.2386    | 0.4885 | 0.4885   |
+| 1.3156        | 2.0   | 148  | 1.1964          | 0.5541 | 0.5627    | 0.575  | 0.575    |
+| 1.0728        | 3.0   | 222  | 1.1077          | 0.6001 | 0.6189    | 0.5981 | 0.5981   |
+| 0.9239        | 4.0   | 296  | 1.0742          | 0.6324 | 0.6361    | 0.6365 | 0.6365   |
+| 0.7802        | 5.0   | 370  | 1.0834          | 0.6073 | 0.6333    | 0.6058 | 0.6058   |
+| 0.661         | 6.0   | 444  | 1.1733          | 0.5984 | 0.6166    | 0.5962 | 0.5962   |
+| 0.602         | 7.0   | 518  | 1.1786          | 0.5911 | 0.6193    | 0.5885 | 0.5885   |
+| 0.5391        | 8.0   | 592  | 1.2171          | 0.6156 | 0.6251    | 0.6154 | 0.6154   |
+| 0.4815        | 9.0   | 666  | 1.2566          | 0.6259 | 0.6399    | 0.625  | 0.625    |
+| 0.4548        | 10.0  | 740  | 1.2927          | 0.6233 | 0.6417    | 0.6212 | 0.6212   |
+| 0.4538        | 11.0  | 814  | 1.2969          | 0.6385 | 0.6461    | 0.6385 | 0.6385   |
+| 0.4119        | 12.0  | 888  | 1.3455          | 0.6376 | 0.6464    | 0.6365 | 0.6365   |
+| 0.3968        | 13.0  | 962  | 1.3709          | 0.6304 | 0.6413    | 0.6288 | 0.6288   |
+| 0.352         | 14.0  | 1036 | 1.3823          | 0.6246 | 0.6360    | 0.6231 | 0.6231   |
+| 0.3551        | 15.0  | 1110 | 1.3914          | 0.6372 | 0.6448    | 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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