File size: 2,987 Bytes
f09301a d31e6a1 f09301a d31e6a1 f09301a d31e6a1 f09301a d31e6a1 f09301a d31e6a1 f09301a d31e6a1 f09301a d31e6a1 f09301a |
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 |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: trainer7
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. -->
# trainer7
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3387
- Precision: 0.7247
- Recall: 0.6905
- F1: 0.6847
- Accuracy: 0.6905
## 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: 5e-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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.8743 | 0.57 | 30 | 1.7616 | 0.1668 | 0.2857 | 0.1788 | 0.2857 |
| 1.7125 | 1.13 | 60 | 1.6249 | 0.2572 | 0.3810 | 0.2914 | 0.3810 |
| 1.4398 | 1.7 | 90 | 1.3244 | 0.4911 | 0.4881 | 0.4326 | 0.4881 |
| 1.0265 | 2.26 | 120 | 1.0496 | 0.6570 | 0.6429 | 0.6197 | 0.6429 |
| 0.6366 | 2.83 | 150 | 0.9035 | 0.6304 | 0.5952 | 0.5764 | 0.5952 |
| 0.3959 | 3.4 | 180 | 0.8226 | 0.6881 | 0.6667 | 0.6557 | 0.6667 |
| 0.2172 | 3.96 | 210 | 1.0152 | 0.6932 | 0.6429 | 0.6356 | 0.6429 |
| 0.0946 | 4.53 | 240 | 1.0485 | 0.7357 | 0.6786 | 0.6913 | 0.6786 |
| 0.0416 | 5.09 | 270 | 1.1458 | 0.6983 | 0.6548 | 0.6565 | 0.6548 |
| 0.0238 | 5.66 | 300 | 1.4215 | 0.6839 | 0.6310 | 0.6272 | 0.6310 |
| 0.0132 | 6.23 | 330 | 1.2009 | 0.7481 | 0.7024 | 0.7090 | 0.7024 |
| 0.0077 | 6.79 | 360 | 1.2686 | 0.6968 | 0.6548 | 0.6538 | 0.6548 |
| 0.0064 | 7.36 | 390 | 1.2725 | 0.7128 | 0.6786 | 0.6717 | 0.6786 |
| 0.0057 | 7.92 | 420 | 1.3092 | 0.7161 | 0.6786 | 0.6731 | 0.6786 |
| 0.0053 | 8.49 | 450 | 1.3306 | 0.7065 | 0.6667 | 0.6640 | 0.6667 |
| 0.0046 | 9.06 | 480 | 1.3377 | 0.7156 | 0.6786 | 0.6749 | 0.6786 |
| 0.0044 | 9.62 | 510 | 1.3387 | 0.7247 | 0.6905 | 0.6847 | 0.6905 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|