Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Seema09/finetuning-sentiment-model-Test")
model = AutoModelForSequenceClassification.from_pretrained("Seema09/finetuning-sentiment-model-Test", device_map="auto")Quick Links
finetuning-sentiment-model-Test
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2703
- Accuracy: 0.904
- F1: 0.9048
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: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Downloads last month
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Dataset used to train Seema09/finetuning-sentiment-model-Test
Evaluation results
- Accuracy on imdbself-reported0.904
- F1 on imdbself-reported0.905
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Seema09/finetuning-sentiment-model-Test")