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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- twitter-sentiment-analysis
metrics:
- accuracy
- f1
model-index:
- name: twitter-sentiment-analysis-v2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: twitter-sentiment-analysis
type: twitter-sentiment-analysis
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8366721507145392
- name: F1
type: f1
value: 0.8366721507145392
---
<!-- 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. -->
# twitter-sentiment-analysis-v2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3771
- Accuracy: 0.8367
- F1: 0.8367
## 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: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3957 | 0.13 | 1000 | 0.4273 | 0.8075 | 0.8005 |
| 0.4086 | 0.27 | 2000 | 0.4081 | 0.8211 | 0.8139 |
| 0.4085 | 0.4 | 3000 | 0.3971 | 0.8274 | 0.8237 |
| 0.3936 | 0.53 | 4000 | 0.3857 | 0.8304 | 0.8307 |
| 0.3783 | 0.67 | 5000 | 0.3978 | 0.8317 | 0.8300 |
| 0.3858 | 0.8 | 6000 | 0.3887 | 0.8281 | 0.8182 |
| 0.3779 | 0.93 | 7000 | 0.3771 | 0.8367 | 0.8367 |
| 0.2971 | 1.07 | 8000 | 0.4023 | 0.8352 | 0.8310 |
| 0.2994 | 1.2 | 9000 | 0.3865 | 0.8326 | 0.8342 |
| 0.293 | 1.33 | 10000 | 0.4454 | 0.8299 | 0.8197 |
| 0.3053 | 1.47 | 11000 | 0.3929 | 0.8364 | 0.8349 |
| 0.3125 | 1.6 | 12000 | 0.4141 | 0.8366 | 0.8314 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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