avsolatorio/doc-topic-model_eval-04_train-02

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: doc-topic-model_eval-04_train-02
+  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. -->
+
+# doc-topic-model_eval-04_train-02
+
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0374
+- Accuracy: 0.9879
+- F1: 0.6272
+- Precision: 0.7299
+- Recall: 0.5498
+
+## 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: 4
+- eval_batch_size: 256
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0944        | 0.4931 | 1000  | 0.0895          | 0.9815   | 0.0    | 0.0       | 0.0    |
+| 0.0769        | 0.9862 | 2000  | 0.0685          | 0.9815   | 0.0014 | 1.0       | 0.0007 |
+| 0.0607        | 1.4793 | 3000  | 0.0560          | 0.9823   | 0.1066 | 0.8022    | 0.0571 |
+| 0.0535        | 1.9724 | 4000  | 0.0501          | 0.9846   | 0.3748 | 0.7494    | 0.2498 |
+| 0.0466        | 2.4655 | 5000  | 0.0450          | 0.9858   | 0.4899 | 0.7338    | 0.3677 |
+| 0.0441        | 2.9586 | 6000  | 0.0421          | 0.9863   | 0.5084 | 0.7553    | 0.3832 |
+| 0.0391        | 3.4517 | 7000  | 0.0404          | 0.9868   | 0.5581 | 0.7311    | 0.4513 |
+| 0.0372        | 3.9448 | 8000  | 0.0393          | 0.9870   | 0.5568 | 0.7564    | 0.4405 |
+| 0.0336        | 4.4379 | 9000  | 0.0382          | 0.9872   | 0.5749 | 0.7485    | 0.4666 |
+| 0.0337        | 4.9310 | 10000 | 0.0375          | 0.9874   | 0.5938 | 0.7375    | 0.4970 |
+| 0.0297        | 5.4241 | 11000 | 0.0368          | 0.9875   | 0.6079 | 0.7260    | 0.5228 |
+| 0.0296        | 5.9172 | 12000 | 0.0376          | 0.9875   | 0.5899 | 0.7526    | 0.4850 |
+| 0.0263        | 6.4103 | 13000 | 0.0372          | 0.9877   | 0.6211 | 0.7210    | 0.5455 |
+| 0.0272        | 6.9034 | 14000 | 0.0376          | 0.9875   | 0.6194 | 0.7061    | 0.5516 |
+| 0.0234        | 7.3964 | 15000 | 0.0373          | 0.9878   | 0.6222 | 0.7304    | 0.5420 |
+| 0.0243        | 7.8895 | 16000 | 0.0374          | 0.9879   | 0.6272 | 0.7299    | 0.5498 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

model.safetensors

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