update README.md

README.md

@@ -8,13 +8,13 @@ model-index:
 - name: Mhammad2023/snli-bert-base-uncased
   results: []
 ---
+# SNLI BERT Base Uncased
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
+<!-- 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 unknown dataset.
+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
@@ -22,6 +22,37 @@ It achieves the following results on the evaluation set:
 - 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
@@ -57,3 +88,25 @@ The following hyperparameters were used during training:
 - 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}")
+```