avsolatorio/doc-topic-model_eval-04_train-01

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-small5tags:6- generated_from_trainer7metrics:8- accuracy9- f110- precision11- recall12model-index:13- name: doc-topic-model_eval-04_train-0114  results: []15---16 17<!-- This model card has been generated automatically according to the information the Trainer had access to. You18should probably proofread and complete it, then remove this comment. -->19 20# doc-topic-model_eval-04_train-0121 22This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.23It achieves the following results on the evaluation set:24- Loss: 0.039625- Accuracy: 0.987926- F1: 0.641527- Precision: 0.712028- Recall: 0.583729 30## Model description31 32More information needed33 34## Intended uses & limitations35 36More information needed37 38## Training and evaluation data39 40More information needed41 42## Training procedure43 44### Training hyperparameters45 46The following hyperparameters were used during training:47- learning_rate: 2e-0548- train_batch_size: 449- eval_batch_size: 25650- seed: 4251- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0852- lr_scheduler_type: linear53- num_epochs: 10054- mixed_precision_training: Native AMP55 56### Training results57 58| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |59|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|60| 0.0941        | 0.4931  | 1000  | 0.0902          | 0.9815   | 0.0    | 0.0       | 0.0    |61| 0.0787        | 0.9862  | 2000  | 0.0703          | 0.9815   | 0.0    | 0.0       | 0.0    |62| 0.0628        | 1.4793  | 3000  | 0.0572          | 0.9823   | 0.1235 | 0.7562    | 0.0672 |63| 0.0537        | 1.9724  | 4000  | 0.0500          | 0.9843   | 0.3220 | 0.7927    | 0.2021 |64| 0.0478        | 2.4655  | 5000  | 0.0466          | 0.9853   | 0.4339 | 0.7566    | 0.3042 |65| 0.0453        | 2.9586  | 6000  | 0.0441          | 0.9859   | 0.5020 | 0.7244    | 0.3841 |66| 0.0389        | 3.4517  | 7000  | 0.0414          | 0.9865   | 0.5425 | 0.7258    | 0.4332 |67| 0.0393        | 3.9448  | 8000  | 0.0406          | 0.9863   | 0.5470 | 0.7070    | 0.4461 |68| 0.0349        | 4.4379  | 9000  | 0.0392          | 0.9870   | 0.5759 | 0.7229    | 0.4786 |69| 0.0344        | 4.9310  | 10000 | 0.0386          | 0.9872   | 0.5807 | 0.7357    | 0.4796 |70| 0.0302        | 5.4241  | 11000 | 0.0381          | 0.9873   | 0.5950 | 0.7282    | 0.5030 |71| 0.0305        | 5.9172  | 12000 | 0.0381          | 0.9872   | 0.5975 | 0.7153    | 0.5129 |72| 0.027         | 6.4103  | 13000 | 0.0378          | 0.9875   | 0.6030 | 0.7290    | 0.5141 |73| 0.0282        | 6.9034  | 14000 | 0.0374          | 0.9876   | 0.6094 | 0.7303    | 0.5229 |74| 0.0235        | 7.3964  | 15000 | 0.0378          | 0.9876   | 0.6213 | 0.7128    | 0.5507 |75| 0.0255        | 7.8895  | 16000 | 0.0372          | 0.9878   | 0.6303 | 0.7188    | 0.5613 |76| 0.0214        | 8.3826  | 17000 | 0.0378          | 0.9878   | 0.6356 | 0.7125    | 0.5737 |77| 0.0222        | 8.8757  | 18000 | 0.0381          | 0.9878   | 0.6313 | 0.7141    | 0.5658 |78| 0.0192        | 9.3688  | 19000 | 0.0390          | 0.9875   | 0.6285 | 0.6951    | 0.5736 |79| 0.0189        | 9.8619  | 20000 | 0.0391          | 0.9878   | 0.6365 | 0.7085    | 0.5778 |80| 0.0159        | 10.3550 | 21000 | 0.0396          | 0.9879   | 0.6415 | 0.7120    | 0.5837 |81 82 83### Framework versions84 85- Transformers 4.44.286- Pytorch 2.4.1+cu12187- Datasets 2.21.088- Tokenizers 0.19.189