from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

checkpoint = "thundarain018/imdb-sentiment-model"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

input_text = "I didn't enjoy this movie at all!"

encoded_input = tokenizer(input_text, return_tensors='pt')
output = model(**encoded_input)
predictions = torch.nn.functional.softmax(output.logits, dim=-1)

print(f"Positive score: {predictions[0][1].item():.2%}")
print(f"Negative score: {predictions[0][0].item():.2%}")

Positive score: 2.48%
Negative score: 97.52%

Alternatively you can use this model with a pipeline directly as well.

Training data

The fine tuning dataset used was the Stanford imdb dataset

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	313	0.3541	0.858
0.2952	2.0	626	0.3340	0.896
0.2952	3.0	939	0.3544	0.905

Framework versions

Transformers 4.50.2
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1