Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+  - natural-language-inference
+  - sentence-transformers
+  - transformers
+  - nlp
+  - model-card
+---
+
+# snowflake-arctic-embed-xs-nli
+
+- **Base Model:** [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
+- **Task:** Natural Language Inference (NLI)
+- **Framework:** Hugging Face Transformers, Sentence Transformers
+
+snowflake-arctic-embed-xs-nli is a fine-tuned NLI model that classifies the relationship between pairs of sentences into three categories: entailment, neutral, and contradiction. It enhances the capabilities of [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) for improved performance on NLI tasks.
+
+## Intended Use
+snowflake-arctic-embed-xs-nli is ideal for applications requiring understanding of logical relationships between sentences, including:
+
+- Semantic textual similarity
+- Question answering
+- Dialogue systems
+- Content moderation
+
+## Performance
+snowflake-arctic-embed-xs-nli was trained on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset, achieving competitive results in sentence pair classification.
+
+Performance on the MNLI matched validation set:
+- Accuracy: 0.7031
+- Precision: 0.71
+- Recall: 0.70
+- F1-score: 0.70
+
+## Training details
+
+<details>
+<summary><strong>Training Details</strong></summary>
+
+- **Dataset:** 
+  - Used [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli).
+  
+- **Sampling:**
+  - 100 000 training samples and 10 000 evaluation samples.
+
+- **Fine-tuning Process:**
+  - Custom Python script with adaptive precision training (bfloat16).
+  - Early stopping based on evaluation loss.
+
+- **Hyperparameters:**
+  - **Learning Rate:** 2e-5
+  - **Batch Size:** 64
+  - **Optimizer:** AdamW (weight decay: 0.01)
+  - **Training Duration:** Up to 10 epochs
+
+</details>
+
+<details>
+<summary><strong>Reproducibility</strong></summary>
+
+To ensure reproducibility:
+- Fixed random seed: 42
+- Environment:
+  - Python: 3.10.12
+  - PyTorch: 2.5.1
+  - Transformers: 4.44.2
+
+</details>
+
+## Usage Instructions
+
+## Using Sentence Transformers
+```python
+from sentence_transformers import CrossEncoder
+
+model_name = "snowflake-arctic-embed-xs-nli"
+model = CrossEncoder(model_name)
+scores = model.predict(
+    [
+        ("A man is eating pizza", "A man eats something"),
+        (
+            "A black race car starts up in front of a crowd of people.",
+            "A man is driving down a lonely road.",
+        ),
+    ]
+)
+
+label_mapping = ["entailment", "neutral", "contradiction"]
+labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
+print(labels)
+# Output: ['entailment', 'contradiction']
+```
+
+## Using Transformers Library
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "snowflake-arctic-embed-xs-nli"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+features = tokenizer(
+    [
+        "A man is eating pizza",
+        "A black race car starts up in front of a crowd of people.",
+    ],
+    ["A man eats something", "A man is driving down a lonely road."],
+    padding=True,
+    truncation=True,
+    return_tensors="pt",
+)
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    label_mapping = ["entailment", "neutral", "contradiction"]
+    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
+    print(labels)
+# Output: ['entailment', 'contradiction']
+```
+
+## Limitations and Ethical Considerations
+snowflake-arctic-embed-xs-nli may reflect biases present in the training data. Users should evaluate its performance in specific contexts to ensure fairness and accuracy.
+
+## Conclusion
+snowflake-arctic-embed-xs-nli offers a robust solution for NLI tasks, enhancing [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)'s capabilities with straightforward integration into existing frameworks. It aids developers in building intelligent applications that require nuanced language understanding.
+