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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: sentiment-analysis-twitter
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: new_dataset
type: new_dataset
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7965
---
<!-- 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. -->
# sentiment-analysis-twitter
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 new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.7965
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5315 | 1.0 | 157 | 0.4517 | 0.788 |
| 0.388 | 2.0 | 314 | 0.4416 | 0.8 |
| 0.3307 | 3.0 | 471 | 0.4579 | 0.7965 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1