metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- text-classification
- transformers
- bert
metrics:
- accuracy
model-index:
- name: bert-finetuned-sst2
results: []
bert-finetuned-sst2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3812
- Accuracy: 0.9083
Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("execbat/bert-finetuned-sst2")
model = AutoModelForSequenceClassification.from_pretrained("execbat/bert-finetuned-sst2")
Use a pipeline as a high-level helper
from transformers import pipeline
label_tags = {'LABEL_0' : "NEGATIVE",
'LABEL_1' : "POSITIVE"}
pipe = pipeline("text-classification", model="execbat/bert-finetuned-sst2")
result = pipe(["what a horrible day!", "what a wonderfull day!"])
encoded_result = [label_tags[i["label"]] for i in result]
print(encoded_result)
['NEGATIVE', 'POSITIVE']
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.269 | 1.0 | 8419 | 0.5041 | 0.8716 |
| 0.1854 | 2.0 | 16838 | 0.4296 | 0.8968 |
| 0.0993 | 3.0 | 25257 | 0.3812 | 0.9083 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.3.2
- Tokenizers 0.21.0