|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-cased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: CONTEXT_one |
|
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. --> |
|
|
|
# CONTEXT_one |
|
|
|
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: 1.1031 |
|
- Precision: 0.8202 |
|
- Recall: 0.8158 |
|
- F1: 0.8134 |
|
- Accuracy: 0.8158 |
|
|
|
## 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 |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 1.3141 | 0.62 | 30 | 1.1728 | 0.4060 | 0.4868 | 0.4214 | 0.4868 | |
|
| 0.8655 | 1.25 | 60 | 0.8567 | 0.7238 | 0.7237 | 0.7207 | 0.7237 | |
|
| 0.6189 | 1.88 | 90 | 0.6433 | 0.7395 | 0.7368 | 0.7361 | 0.7368 | |
|
| 0.4575 | 2.5 | 120 | 0.6314 | 0.7661 | 0.7632 | 0.7625 | 0.7632 | |
|
| 0.3123 | 3.12 | 150 | 0.6091 | 0.7636 | 0.7632 | 0.7621 | 0.7632 | |
|
| 0.215 | 3.75 | 180 | 0.6095 | 0.7769 | 0.7763 | 0.7758 | 0.7763 | |
|
| 0.2901 | 4.38 | 210 | 0.6833 | 0.7409 | 0.7368 | 0.7367 | 0.7368 | |
|
| 0.2169 | 5.0 | 240 | 0.6651 | 0.8354 | 0.8289 | 0.8285 | 0.8289 | |
|
| 0.1721 | 5.62 | 270 | 0.6578 | 0.8530 | 0.8421 | 0.8416 | 0.8421 | |
|
| 0.2103 | 6.25 | 300 | 0.7525 | 0.7506 | 0.75 | 0.7481 | 0.75 | |
|
| 0.1021 | 6.88 | 330 | 0.6357 | 0.8725 | 0.8684 | 0.8681 | 0.8684 | |
|
| 0.1115 | 7.5 | 360 | 1.0796 | 0.7510 | 0.75 | 0.7452 | 0.75 | |
|
| 0.05 | 8.12 | 390 | 0.6933 | 0.8444 | 0.8289 | 0.8264 | 0.8289 | |
|
| 0.0419 | 8.75 | 420 | 0.7248 | 0.8295 | 0.8158 | 0.8135 | 0.8158 | |
|
| 0.0521 | 9.38 | 450 | 1.0193 | 0.7867 | 0.7895 | 0.7848 | 0.7895 | |
|
| 0.0197 | 10.0 | 480 | 0.7878 | 0.7867 | 0.7895 | 0.7848 | 0.7895 | |
|
| 0.0165 | 10.62 | 510 | 1.3815 | 0.7232 | 0.7105 | 0.6969 | 0.7105 | |
|
| 0.0321 | 11.25 | 540 | 0.9198 | 0.7867 | 0.7895 | 0.7848 | 0.7895 | |
|
| 0.0202 | 11.88 | 570 | 0.9919 | 0.8044 | 0.8026 | 0.7993 | 0.8026 | |
|
| 0.0046 | 12.5 | 600 | 1.1230 | 0.7622 | 0.7632 | 0.7528 | 0.7632 | |
|
| 0.0044 | 13.12 | 630 | 0.8484 | 0.8579 | 0.8553 | 0.8551 | 0.8553 | |
|
| 0.0019 | 13.75 | 660 | 1.0979 | 0.7925 | 0.7895 | 0.7855 | 0.7895 | |
|
| 0.0018 | 14.38 | 690 | 1.3561 | 0.7480 | 0.75 | 0.7438 | 0.75 | |
|
| 0.0021 | 15.0 | 720 | 1.0228 | 0.8006 | 0.8026 | 0.7991 | 0.8026 | |
|
| 0.0014 | 15.62 | 750 | 0.9298 | 0.8422 | 0.8421 | 0.8413 | 0.8421 | |
|
| 0.0014 | 16.25 | 780 | 0.9537 | 0.8276 | 0.8289 | 0.8274 | 0.8289 | |
|
| 0.0012 | 16.88 | 810 | 0.9708 | 0.8276 | 0.8289 | 0.8274 | 0.8289 | |
|
| 0.0013 | 17.5 | 840 | 1.0009 | 0.8276 | 0.8289 | 0.8274 | 0.8289 | |
|
| 0.0011 | 18.12 | 870 | 0.9999 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.0011 | 18.75 | 900 | 0.9871 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.001 | 19.38 | 930 | 0.9885 | 0.8276 | 0.8289 | 0.8274 | 0.8289 | |
|
| 0.001 | 20.0 | 960 | 1.0078 | 0.8276 | 0.8289 | 0.8274 | 0.8289 | |
|
| 0.0009 | 20.62 | 990 | 1.0204 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.0008 | 21.25 | 1020 | 1.0312 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.0008 | 21.88 | 1050 | 1.0438 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.0008 | 22.5 | 1080 | 1.0647 | 0.8037 | 0.8026 | 0.7997 | 0.8026 | |
|
| 0.0008 | 23.12 | 1110 | 1.0633 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 23.75 | 1140 | 1.0661 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0008 | 24.38 | 1170 | 1.0871 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 25.0 | 1200 | 1.0965 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 25.62 | 1230 | 1.0893 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 26.25 | 1260 | 1.0935 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 26.88 | 1290 | 1.0942 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 27.5 | 1320 | 1.0949 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 28.12 | 1350 | 1.0937 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 28.75 | 1380 | 1.0986 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 29.38 | 1410 | 1.1030 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
| 0.0007 | 30.0 | 1440 | 1.1031 | 0.8202 | 0.8158 | 0.8134 | 0.8158 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.1 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.1 |
|
|
