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---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Mhammad2023/snli-bert-base-uncased
results: []
---
# SNLI BERT Base Uncased
<!-- This model is a fine-tuned **BERT-base-uncased** transformer on the **Stanford Natural Language Inference (SNLI)** dataset. It performs **natural language inference** (NLI), also known as recognizing textual entailment (RTE), which involves classifying whether a *hypothesis* sentence is entailed by, contradicts, or is neutral with respect to a *premise* sentence. -->
# Mhammad2023/snli-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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}")
```
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