update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- accuracy
|
| 7 |
+
- f1
|
| 8 |
+
model-index:
|
| 9 |
+
- name: bert-base-uncased-ft-news
|
| 10 |
+
results: []
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 14 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 15 |
+
|
| 16 |
+
# bert-base-uncased-ft-news
|
| 17 |
+
|
| 18 |
+
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
|
| 19 |
+
It achieves the following results on the evaluation set:
|
| 20 |
+
- Loss: 0.4300
|
| 21 |
+
- Accuracy: 0.9
|
| 22 |
+
- F1: 0.8783
|
| 23 |
+
|
| 24 |
+
## Model description
|
| 25 |
+
|
| 26 |
+
More information needed
|
| 27 |
+
|
| 28 |
+
## Intended uses & limitations
|
| 29 |
+
|
| 30 |
+
More information needed
|
| 31 |
+
|
| 32 |
+
## Training and evaluation data
|
| 33 |
+
|
| 34 |
+
More information needed
|
| 35 |
+
|
| 36 |
+
## Training procedure
|
| 37 |
+
|
| 38 |
+
### Training hyperparameters
|
| 39 |
+
|
| 40 |
+
The following hyperparameters were used during training:
|
| 41 |
+
- learning_rate: 2e-05
|
| 42 |
+
- train_batch_size: 16
|
| 43 |
+
- eval_batch_size: 16
|
| 44 |
+
- seed: 42
|
| 45 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 46 |
+
- lr_scheduler_type: linear
|
| 47 |
+
- num_epochs: 3
|
| 48 |
+
- mixed_precision_training: Native AMP
|
| 49 |
+
|
| 50 |
+
### Training results
|
| 51 |
+
|
| 52 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|
| 53 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
|
| 54 |
+
| 0.4196 | 0.37 | 120 | 0.3051 | 0.8875 | 0.8566 |
|
| 55 |
+
| 0.3101 | 0.75 | 240 | 0.2979 | 0.8953 | 0.8743 |
|
| 56 |
+
| 0.2693 | 1.12 | 360 | 0.3162 | 0.9016 | 0.8831 |
|
| 57 |
+
| 0.2078 | 1.5 | 480 | 0.3298 | 0.8984 | 0.8767 |
|
| 58 |
+
| 0.1725 | 1.87 | 600 | 0.3801 | 0.9047 | 0.8851 |
|
| 59 |
+
| 0.1369 | 2.24 | 720 | 0.3901 | 0.8938 | 0.8677 |
|
| 60 |
+
| 0.1101 | 2.62 | 840 | 0.4160 | 0.9016 | 0.8805 |
|
| 61 |
+
| 0.1019 | 2.99 | 960 | 0.4300 | 0.9 | 0.8783 |
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
### Framework versions
|
| 65 |
+
|
| 66 |
+
- Transformers 4.25.1
|
| 67 |
+
- Pytorch 1.13.0+cu116
|
| 68 |
+
- Datasets 2.8.0
|
| 69 |
+
- Tokenizers 0.13.2
|