File size: 3,718 Bytes
363ce87 |
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 87 88 89 90 |
---
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-cased-lft
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. -->
# distilbert-cased-lft
This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1017
- Precision: 0.8722
- Recall: 0.8905
- F1: 0.8813
- Accuracy: 0.9764
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.4065 | 100 | 0.1106 | 0.8374 | 0.7969 | 0.8167 | 0.9647 |
| No log | 0.8130 | 200 | 0.0926 | 0.8242 | 0.8474 | 0.8356 | 0.9690 |
| No log | 1.2195 | 300 | 0.0898 | 0.8325 | 0.8671 | 0.8494 | 0.9704 |
| No log | 1.6260 | 400 | 0.0873 | 0.8591 | 0.8614 | 0.8602 | 0.9729 |
| 0.0943 | 2.0325 | 500 | 0.0829 | 0.8563 | 0.8765 | 0.8662 | 0.9740 |
| 0.0943 | 2.4390 | 600 | 0.0864 | 0.8656 | 0.8747 | 0.8701 | 0.9746 |
| 0.0943 | 2.8455 | 700 | 0.0842 | 0.8652 | 0.8761 | 0.8706 | 0.9746 |
| 0.0943 | 3.2520 | 800 | 0.0875 | 0.8627 | 0.8823 | 0.8724 | 0.9746 |
| 0.0943 | 3.6585 | 900 | 0.0887 | 0.8564 | 0.8829 | 0.8694 | 0.9744 |
| 0.0444 | 4.0650 | 1000 | 0.0875 | 0.8801 | 0.8797 | 0.8799 | 0.9763 |
| 0.0444 | 4.4715 | 1100 | 0.0944 | 0.8516 | 0.8901 | 0.8704 | 0.9746 |
| 0.0444 | 4.8780 | 1200 | 0.0906 | 0.8607 | 0.8891 | 0.8746 | 0.9752 |
| 0.0444 | 5.2846 | 1300 | 0.0934 | 0.8706 | 0.8896 | 0.8800 | 0.9765 |
| 0.0444 | 5.6911 | 1400 | 0.0914 | 0.8784 | 0.8862 | 0.8823 | 0.9765 |
| 0.0248 | 6.0976 | 1500 | 0.0918 | 0.8796 | 0.8896 | 0.8846 | 0.9772 |
| 0.0248 | 6.5041 | 1600 | 0.0960 | 0.8711 | 0.8916 | 0.8812 | 0.9765 |
| 0.0248 | 6.9106 | 1700 | 0.0970 | 0.8678 | 0.8876 | 0.8776 | 0.9763 |
| 0.0248 | 7.3171 | 1800 | 0.1008 | 0.8690 | 0.8887 | 0.8787 | 0.9759 |
| 0.0248 | 7.7236 | 1900 | 0.1012 | 0.8650 | 0.8926 | 0.8786 | 0.9759 |
| 0.0153 | 8.1301 | 2000 | 0.1002 | 0.8715 | 0.8921 | 0.8817 | 0.9762 |
| 0.0153 | 8.5366 | 2100 | 0.1003 | 0.8749 | 0.8889 | 0.8818 | 0.9763 |
| 0.0153 | 8.9431 | 2200 | 0.1015 | 0.8680 | 0.8917 | 0.8797 | 0.9760 |
| 0.0153 | 9.3496 | 2300 | 0.1015 | 0.8716 | 0.8882 | 0.8798 | 0.9764 |
| 0.0153 | 9.7561 | 2400 | 0.1017 | 0.8722 | 0.8905 | 0.8813 | 0.9764 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|