File size: 1,916 Bytes
d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 d8b5db9 067ded2 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.911697247706422
---
<!-- 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-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3574
- Accuracy: 0.9117
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1867 | 1.0 | 4210 | 0.3226 | 0.9071 |
| 0.1277 | 2.0 | 8420 | 0.3630 | 0.9037 |
| 0.1027 | 3.0 | 12630 | 0.3574 | 0.9117 |
| 0.0659 | 4.0 | 16840 | 0.4427 | 0.9048 |
| 0.0334 | 5.0 | 21050 | 0.4979 | 0.9083 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|