File size: 2,013 Bytes
0403079 |
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 |
---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- recall
- accuracy
- precision
model-index:
- name: mdeberta-v3-base-fine-tuned-text-classificarion-ds-ss
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. -->
# mdeberta-v3-base-fine-tuned-text-classificarion-ds-ss
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0388
- F1: 0.7346
- Recall: 0.7570
- Accuracy: 0.7570
- Precision: 0.7456
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:---------:|
| 3.3067 | 1.0 | 883 | 1.8916 | 0.4861 | 0.5479 | 0.5479 | 0.4605 |
| 1.5839 | 2.0 | 1766 | 1.2443 | 0.6494 | 0.6955 | 0.6955 | 0.6456 |
| 1.1286 | 3.0 | 2649 | 1.0806 | 0.7047 | 0.7283 | 0.7283 | 0.7074 |
| 0.892 | 4.0 | 3532 | 1.0966 | 0.7087 | 0.7393 | 0.7393 | 0.7165 |
| 0.7316 | 5.0 | 4415 | 1.0388 | 0.7346 | 0.7570 | 0.7570 | 0.7456 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|