File size: 3,757 Bytes
5a7aa1d 38e8852 5a7aa1d 38e8852 5a7aa1d 38e8852 5a7aa1d 38e8852 5a7aa1d |
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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: distilbert_base_SST2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8979357798165137
---
<!-- 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_base_SST2
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- Accuracy: 0.8979
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1303 | 0.06 | 500 | 0.6599 | 0.8784 |
| 0.1781 | 0.12 | 1000 | 0.5644 | 0.8716 |
| 0.1856 | 0.18 | 1500 | 0.5357 | 0.8716 |
| 0.1833 | 0.24 | 2000 | 0.6749 | 0.8727 |
| 0.1887 | 0.3 | 2500 | 0.4967 | 0.8945 |
| 0.1873 | 0.36 | 3000 | 0.6415 | 0.8658 |
| 0.1787 | 0.42 | 3500 | 0.4886 | 0.9014 |
| 0.1979 | 0.48 | 4000 | 0.4122 | 0.8865 |
| 0.1662 | 0.53 | 4500 | 0.5310 | 0.8888 |
| 0.1718 | 0.59 | 5000 | 0.5415 | 0.8945 |
| 0.1808 | 0.65 | 5500 | 0.4059 | 0.8956 |
| 0.1666 | 0.71 | 6000 | 0.4731 | 0.8876 |
| 0.1762 | 0.77 | 6500 | 0.3817 | 0.8807 |
| 0.1782 | 0.83 | 7000 | 0.4583 | 0.8956 |
| 0.1739 | 0.89 | 7500 | 0.4756 | 0.8888 |
| 0.1715 | 0.95 | 8000 | 0.4871 | 0.8911 |
| 0.1682 | 1.01 | 8500 | 0.4936 | 0.8922 |
| 0.095 | 1.07 | 9000 | 0.4956 | 0.8899 |
| 0.0928 | 1.13 | 9500 | 0.6543 | 0.8716 |
| 0.0855 | 1.19 | 10000 | 0.5812 | 0.8956 |
| 0.1032 | 1.25 | 10500 | 0.6683 | 0.8716 |
| 0.0982 | 1.31 | 11000 | 0.6076 | 0.8842 |
| 0.0907 | 1.37 | 11500 | 0.5826 | 0.8956 |
| 0.1085 | 1.43 | 12000 | 0.4708 | 0.8922 |
| 0.0785 | 1.48 | 12500 | 0.5486 | 0.8956 |
| 0.0903 | 1.54 | 13000 | 0.6104 | 0.875 |
| 0.0764 | 1.6 | 13500 | 0.5576 | 0.8888 |
| 0.0982 | 1.66 | 14000 | 0.5447 | 0.8888 |
| 0.0864 | 1.72 | 14500 | 0.4833 | 0.8922 |
| 0.0888 | 1.78 | 15000 | 0.4737 | 0.8945 |
| 0.0775 | 1.84 | 15500 | 0.4818 | 0.8991 |
| 0.0958 | 1.9 | 16000 | 0.4674 | 0.8991 |
| 0.0805 | 1.96 | 16500 | 0.4747 | 0.8979 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
|