File size: 3,246 Bytes
7bcd401 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
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-02_train-04
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-02_train-04
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: 0.0381
- Accuracy: 0.9879
- F1: 0.6381
- Precision: 0.7172
- Recall: 0.5747
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0934 | 0.4929 | 1000 | 0.0904 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0778 | 0.9857 | 2000 | 0.0702 | 0.9814 | 0.0 | 0.0 | 0.0 |
| 0.0618 | 1.4786 | 3000 | 0.0567 | 0.9828 | 0.1751 | 0.8200 | 0.0980 |
| 0.0535 | 1.9714 | 4000 | 0.0492 | 0.9843 | 0.3358 | 0.7842 | 0.2136 |
| 0.0473 | 2.4643 | 5000 | 0.0455 | 0.9855 | 0.4668 | 0.7407 | 0.3408 |
| 0.0436 | 2.9571 | 6000 | 0.0426 | 0.9861 | 0.5016 | 0.7524 | 0.3763 |
| 0.0389 | 3.4500 | 7000 | 0.0407 | 0.9865 | 0.5337 | 0.7497 | 0.4143 |
| 0.0376 | 3.9428 | 8000 | 0.0399 | 0.9866 | 0.5634 | 0.7187 | 0.4633 |
| 0.0339 | 4.4357 | 9000 | 0.0389 | 0.9870 | 0.5653 | 0.7472 | 0.4547 |
| 0.0337 | 4.9285 | 10000 | 0.0385 | 0.9873 | 0.5815 | 0.7552 | 0.4728 |
| 0.0295 | 5.4214 | 11000 | 0.0377 | 0.9872 | 0.6024 | 0.7156 | 0.5202 |
| 0.0305 | 5.9142 | 12000 | 0.0383 | 0.9874 | 0.5992 | 0.7317 | 0.5074 |
| 0.0254 | 6.4071 | 13000 | 0.0375 | 0.9876 | 0.6141 | 0.7281 | 0.5310 |
| 0.0273 | 6.9000 | 14000 | 0.0379 | 0.9877 | 0.6163 | 0.7325 | 0.5319 |
| 0.0228 | 7.3928 | 15000 | 0.0379 | 0.9877 | 0.6165 | 0.7367 | 0.5300 |
| 0.0235 | 7.8857 | 16000 | 0.0379 | 0.9874 | 0.6298 | 0.6930 | 0.5773 |
| 0.0208 | 8.3785 | 17000 | 0.0379 | 0.9877 | 0.6341 | 0.7129 | 0.5710 |
| 0.0204 | 8.8714 | 18000 | 0.0381 | 0.9879 | 0.6381 | 0.7172 | 0.5747 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|