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---
license: apache-2.0
base_model: distilbert-base-uncased
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: 0.8990825688073395
---
<!-- 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](https://huggingface.co/distilbert-base-uncased) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4181
- Accuracy: 0.8991
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4378 | 0.06 | 500 | 0.3452 | 0.8601 |
| 0.343 | 0.12 | 1000 | 0.3483 | 0.8578 |
| 0.3342 | 0.18 | 1500 | 0.3373 | 0.8704 |
| 0.308 | 0.24 | 2000 | 0.4102 | 0.8819 |
| 0.2932 | 0.3 | 2500 | 0.3546 | 0.8830 |
| 0.3116 | 0.36 | 3000 | 0.3609 | 0.8716 |
| 0.2805 | 0.42 | 3500 | 0.3800 | 0.8945 |
| 0.2655 | 0.48 | 4000 | 0.4131 | 0.8842 |
| 0.2504 | 0.53 | 4500 | 0.4299 | 0.8830 |
| 0.2543 | 0.59 | 5000 | 0.5196 | 0.8727 |
| 0.2409 | 0.65 | 5500 | 0.4387 | 0.8807 |
| 0.2414 | 0.71 | 6000 | 0.4121 | 0.8922 |
| 0.2319 | 0.77 | 6500 | 0.3772 | 0.8830 |
| 0.247 | 0.83 | 7000 | 0.4179 | 0.8876 |
| 0.2233 | 0.89 | 7500 | 0.3544 | 0.8945 |
| 0.2202 | 0.95 | 8000 | 0.4160 | 0.8865 |
| 0.2242 | 1.01 | 8500 | 0.5125 | 0.8784 |
| 0.1296 | 1.07 | 9000 | 0.4212 | 0.8842 |
| 0.1429 | 1.13 | 9500 | 0.4675 | 0.8968 |
| 0.1466 | 1.19 | 10000 | 0.5034 | 0.8922 |
| 0.1626 | 1.25 | 10500 | 0.4431 | 0.8945 |
| 0.1459 | 1.31 | 11000 | 0.5001 | 0.8922 |
| 0.1489 | 1.37 | 11500 | 0.4739 | 0.8968 |
| 0.1591 | 1.43 | 12000 | 0.3852 | 0.8945 |
| 0.1211 | 1.48 | 12500 | 0.4648 | 0.8945 |
| 0.1275 | 1.54 | 13000 | 0.5281 | 0.8956 |
| 0.1302 | 1.6 | 13500 | 0.4411 | 0.8933 |
| 0.1313 | 1.66 | 14000 | 0.4914 | 0.8979 |
| 0.134 | 1.72 | 14500 | 0.3923 | 0.8979 |
| 0.1355 | 1.78 | 15000 | 0.4164 | 0.8956 |
| 0.1263 | 1.84 | 15500 | 0.4293 | 0.8945 |
| 0.1326 | 1.9 | 16000 | 0.4185 | 0.8933 |
| 0.1315 | 1.96 | 16500 | 0.4181 | 0.8991 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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