Create README.md

README.md

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+---
+license: mit
+language:
+- en
+- pt
+base_model:
+- cnmoro/tangled-llama-33m-32k-instruct-v0.1-fix
+pipeline_tag: text-classification
+---
+
+```python
+from tokenizers import Tokenizer
+import onnxruntime as ort
+import numpy as np
+
+reranker_tokenizer = Tokenizer.from_file('./tokenizer.json')
+reranker_session = ort.InferenceSession('./model.onnx')
+
+def rerank(question, passages, normalize_scores=True):
+    # Format input templates
+    templates = [f"Query: {question}\nSentence: {passage}" for passage in passages]
+    encoded_inputs = reranker_tokenizer.encode_batch(templates)
+
+    # Convert to lists and truncate sequences to max length (512)
+    input_ids = [enc.ids[:512] for enc in encoded_inputs]  # Truncate here
+    attention_mask = [[1] * len(ids) for ids in input_ids]
+    # token_type_ids = [[0] * len(ids) for ids in input_ids]
+
+    # Find max length in batch
+    batch_max_length = max(len(ids) for ids in input_ids)  # Already truncated to <=512
+
+    # Pad sequences
+    def pad_sequence(seq, pad_value=0):
+        return seq + [pad_value] * (batch_max_length - len(seq))
+
+    input_ids = np.array([pad_sequence(ids) for ids in input_ids], dtype=np.int64)
+    attention_mask = np.array([pad_sequence(mask, pad_value=0) for mask in attention_mask], dtype=np.int64)
+    # token_type_ids = np.array([pad_sequence(types, pad_value=0) for types in token_type_ids], dtype=np.int64)
+
+    # Create ONNX input dict
+    inputs_onnx = {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        # "token_type_ids": token_type_ids
+    }
+
+    # Run ONNX model
+    outputs = reranker_session.run(None, inputs_onnx)
+    logits = outputs[0]
+
+    # Apply softmax to get probabilities
+    probabilities = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
+
+    # Get predicted class and confidence score
+    predicted_classes = np.argmax(probabilities, axis=1).tolist()
+    confidences = np.max(probabilities, axis=1).tolist()
+
+    results = [
+        {"passage": passage, "prediction": pred, "confidence": conf}
+        for passage, pred, conf in zip(passages, predicted_classes, confidences)
+    ]
+
+    final_results = []
+    for document, result in zip(passages, results):
+        # If the prediction is 0, adjust the confidence score
+        if result['prediction'] == 0:
+            result['confidence'] = 1 - result['confidence']
+        final_results.append((document, result['confidence']))
+    
+    # Sort by confidence score in descending order
+    sorted_results = sorted(final_results, key=lambda x: x[1], reverse=True)
+
+    # Normalize scores if required
+    if normalize_scores:
+        total_score = sum(result[1] for result in sorted_results)
+        if total_score > 0:
+            sorted_results = [(result[0], result[1] / total_score) for result in sorted_results]
+
+    return sorted_results
+
+question = "O que é o Pantanal?"
+passages = [
+    "É um dos ecossistemas mais ricos em biodiversidade do mundo, abrigando uma grande variedade de espécies animais e vegetais.",
+    "Sua beleza natural, com rios e lagos interligados, atrai turistas de todo o mundo.",
+    "O Pantanal sofre com impactos ambientais, como a exploração mineral e o desmatamento.",
+    "O Pantanal é uma extensa planície alagável localizada na América do Sul, principalmente no Brasil, mas também em partes da Bolívia e Paraguai.",
+    "É um local com importância histórica e cultural para as populações locais.",
+    "O Pantanal é um importante habitat para diversas espécies de animais, inclusive aves migratórias."
+]
+ranked_results = rerank(question, passages, normalize_scores=True)
+ranked_results
+# [('O Pantanal é uma extensa planície alagável localizada na América do Sul, principalmente no Brasil, mas também em partes da Bolívia e Paraguai.',
+#   0.7105862286443647),
+#  ('O Pantanal é um importante habitat para diversas espécies de animais, inclusive aves migratórias.',
+#   0.22660008031497725),
+#  ('O Pantanal sofre com impactos ambientais, como a exploração mineral e o desmatamento.',
+#   0.043374300040060654),
+#  ('É um local com importância histórica e cultural para as populações locais.',
+#   0.0070428120274147726),
+#  ('É um dos ecossistemas mais ricos em biodiversidade do mundo, abrigando uma grande variedade de espécies animais e vegetais.',
+#   0.006359544027065005),
+#  ('Sua beleza natural, com rios e lagos interligados, atrai turistas de todo o mundo.',
+#   0.006037034946117598)]
+
+question = "What is the speed of light?"
+passages = [
+    "Isaac Newton's laws of motion and gravity laid the groundwork for classical mechanics.",
+    "The theory of relativity, proposed by Albert Einstein, has revolutionized our understanding of space, time, and gravity.",
+    "The Earth orbits the Sun at an average distance of about 93 million miles, taking roughly 365.25 days to complete one revolution.",
+    "The speed of light in a vacuum is approximately 299,792 kilometers per second (km/s), or about 186,282 miles per second.",
+    "Light can be described as both a wave and a particle, a concept known as wave-particle duality."
+]
+ranked_results = rerank(question, passages, normalize_scores=True)
+ranked_results
+# [('The speed of light in a vacuum is approximately 299,792 kilometers per second (km/s), or about 186,282 miles per second.',
+#   0.5686758878772575),
+#  ('The theory of relativity, proposed by Albert Einstein, has revolutionized our understanding of space, time, and gravity.',
+#   0.14584055128478327),
+#  ('The Earth orbits the Sun at an average distance of about 93 million miles, taking roughly 365.25 days to complete one revolution.',
+#   0.13790743024424898),
+#  ("Isaac Newton's laws of motion and gravity laid the groundwork for classical mechanics.",
+#   0.08071345159269593),
+#  ('Light can be described as both a wave and a particle, a concept known as wave-particle duality.',
+#   0.06686267900101434)]
+```