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

python

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