Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("indukurs/pruned_model")
model = AutoModelForSequenceClassification.from_pretrained("indukurs/pruned_model", device_map="auto")Quick Links
pruned_model
This model is a fine-tuned version of bert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.3204
- Accuracy: 0.9
- F1: 0.9
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 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for indukurs/pruned_model
Base model
google-bert/bert-base-uncasedDataset used to train indukurs/pruned_model
Evaluation results
- Accuracy on imdbtest set self-reported0.900
- F1 on imdbtest set self-reported0.900
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="indukurs/pruned_model")