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Mhammad2023/
snli-bert-base-uncased

Name: snli-bert-base-uncased
Availability: InStock

transformers

bert

text-classification

generated_from_keras_callback

google-bert/bert-base-uncased

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SNLI BERT Base Uncased

Mhammad2023/snli-bert-base-uncased

This model is a fine-tuned version of bert-base-uncased on an the Stanford Natural Language Inference (SNLI) dataset. It achieves the following results on the evaluation set:

Train Loss: 0.3830
Train Accuracy: 0.8599
Validation Loss: 0.3341
Validation Accuracy: 0.8746
Epoch: 2

Model Details

Architecture: BERT-base (uncased)
Dataset: Stanford Natural Language Inference (SNLI)
Labels:
- entailment (0)
- neutral (1)
- contradiction (2)
Framework: PyTorch
Tokenizer: bert-base-uncased

Intended Use

This model can be used for:

Textual entailment tasks
Sentence pair classification
Natural language understanding tasks requiring inference

Limitations and Biases

The model inherits any biases present in the original SNLI dataset.

It may not generalize well to domains or sentence pairs that are significantly different from the SNLI training data.

Performance may degrade on noisy or complex linguistic inputs.

Training Data

The model is fine-tuned on the Stanford Natural Language Inference (SNLI) dataset:

SNLI Dataset: https://huggingface.co/datasets/stanfordnlp/snli

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 13500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.6102	0.7444	0.4452	0.8295	0
0.4504	0.8280	0.3723	0.8600	1
0.3830	0.8599	0.3341	0.8746	2

Framework versions

Transformers 4.52.2
TensorFlow 2.18.0
Datasets 3.6.0
Tokenizers 0.21.1

How to Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("Mhammad2023/snli-bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("Mhammad2023/snli-bert-base-uncased")

premise = "A man inspects the uniform of a figure in some East Asian country."
hypothesis = "The man is sleeping."

inputs = tokenizer(premise, hypothesis, return_tensors="pt")
outputs = model(**inputs)
probs = F.softmax(outputs.logits, dim=1)
predicted_class = torch.argmax(probs).item()

label_map = {0: "entailment", 1: "neutral", 2: "contradiction"}
print(f"Prediction: {label_map[predicted_class]} with confidence {probs[0][predicted_class].item():.4f}")