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base_model: google-bert/bert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-sst2-sentiment-full
results: []
---
<!-- 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. -->
# bert-sst2-sentiment-full
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3013
- Accuracy: 0.9197
- F1: 0.9201
- Precision: 0.9329
- Recall: 0.9077
## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1068 | 1.0 | 4210 | 0.2625 | 0.9128 | 0.9116 | 0.9423 | 0.8829 |
| 0.1025 | 2.0 | 8420 | 0.3288 | 0.9094 | 0.9133 | 0.8908 | 0.9369 |
| 0.0646 | 3.0 | 12630 | 0.3013 | 0.9197 | 0.9201 | 0.9329 | 0.9077 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|