avsolatorio/doc-topic-model_eval-00_train-01

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-00_train-01
+  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-00_train-01
+
+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: 0.0398
+- Accuracy: 0.9878
+- F1: 0.6321
+- Precision: 0.7134
+- Recall: 0.5675
+
+## 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 |
+|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0936        | 0.4931 | 1000  | 0.0882          | 0.9815   | 0.0    | 0.0       | 0.0    |
+| 0.0754        | 0.9862 | 2000  | 0.0682          | 0.9815   | 0.0006 | 0.5714    | 0.0003 |
+| 0.0614        | 1.4793 | 3000  | 0.0561          | 0.9824   | 0.1463 | 0.7360    | 0.0812 |
+| 0.053         | 1.9724 | 4000  | 0.0500          | 0.9842   | 0.3207 | 0.7946    | 0.2009 |
+| 0.0477        | 2.4655 | 5000  | 0.0463          | 0.9853   | 0.4453 | 0.7381    | 0.3189 |
+| 0.0445        | 2.9586 | 6000  | 0.0435          | 0.9859   | 0.4832 | 0.7548    | 0.3553 |
+| 0.0385        | 3.4517 | 7000  | 0.0410          | 0.9865   | 0.5406 | 0.7356    | 0.4273 |
+| 0.0384        | 3.9448 | 8000  | 0.0400          | 0.9867   | 0.5643 | 0.7201    | 0.4639 |
+| 0.0347        | 4.4379 | 9000  | 0.0386          | 0.9870   | 0.5796 | 0.7235    | 0.4834 |
+| 0.0336        | 4.9310 | 10000 | 0.0381          | 0.9873   | 0.5971 | 0.7223    | 0.5089 |
+| 0.0299        | 5.4241 | 11000 | 0.0374          | 0.9875   | 0.5941 | 0.7483    | 0.4926 |
+| 0.0299        | 5.9172 | 12000 | 0.0375          | 0.9874   | 0.5978 | 0.7279    | 0.5071 |
+| 0.0265        | 6.4103 | 13000 | 0.0377          | 0.9874   | 0.6035 | 0.7218    | 0.5185 |
+| 0.0271        | 6.9034 | 14000 | 0.0379          | 0.9872   | 0.6061 | 0.7061    | 0.5309 |
+| 0.0229        | 7.3964 | 15000 | 0.0373          | 0.9877   | 0.6254 | 0.7162    | 0.5550 |
+| 0.0245        | 7.8895 | 16000 | 0.0378          | 0.9879   | 0.6295 | 0.7266    | 0.5553 |
+| 0.0205        | 8.3826 | 17000 | 0.0376          | 0.9876   | 0.6300 | 0.7041    | 0.5701 |
+| 0.0213        | 8.8757 | 18000 | 0.0385          | 0.9878   | 0.6303 | 0.7156    | 0.5631 |
+| 0.0183        | 9.3688 | 19000 | 0.0389          | 0.9878   | 0.6300 | 0.7164    | 0.5621 |
+| 0.0182        | 9.8619 | 20000 | 0.0398          | 0.9878   | 0.6321 | 0.7134    | 0.5675 |
+
+
+### 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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