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---
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: 0.9151376146788991
---
<!-- 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: 0.3690
- Accuracy: 0.9151
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1335 | 0.06 | 500 | 0.5579 | 0.8911 |
| 0.1666 | 0.12 | 1000 | 0.5413 | 0.8876 |
| 0.1778 | 0.18 | 1500 | 0.7077 | 0.8544 |
| 0.1746 | 0.24 | 2000 | 0.5727 | 0.875 |
| 0.1632 | 0.3 | 2500 | 0.4972 | 0.8979 |
| 0.1675 | 0.36 | 3000 | 0.4742 | 0.8991 |
| 0.1573 | 0.42 | 3500 | 0.4943 | 0.8956 |
| 0.1525 | 0.48 | 4000 | 0.4907 | 0.8819 |
| 0.1394 | 0.53 | 4500 | 0.5010 | 0.8899 |
| 0.1458 | 0.59 | 5000 | 0.5461 | 0.8876 |
| 0.1588 | 0.65 | 5500 | 0.3364 | 0.9094 |
| 0.1373 | 0.71 | 6000 | 0.4198 | 0.9163 |
| 0.138 | 0.77 | 6500 | 0.3466 | 0.9128 |
| 0.1383 | 0.83 | 7000 | 0.4064 | 0.9094 |
| 0.1371 | 0.89 | 7500 | 0.4083 | 0.9002 |
| 0.1373 | 0.95 | 8000 | 0.3690 | 0.9151 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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