config.json inference.py

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_size": 312,
+  "initializer_range": 0.02,
+  "intermediate_size": 1200,
+  "max_position_embeddings": 512,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 4,
+  "type_vocab_size": 2,
+  "vocab_size": 30522,
+  "model_type": "bert",
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "layer_norm_eps": 1e-12,
+  "use_cache": true
+}

inference.py

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+import torch
+from transformers import AutoTokenizer
+from fin_tinybert_pytorch import TinyFinBERTRegressor  # You may need to rename or include this class here
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = TinyFinBERTRegressor()
+model.load_state_dict(torch.load("./saved_model/pytorch_model.bin", map_location=device))
+model.to(device)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained("./saved_model")
+
+def predict(texts):
+    if isinstance(texts, str):
+        texts = [texts]
+
+    results = []
+    for text in texts:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding='max_length', max_length=128)
+        inputs = {k: v.to(device) for k, v in inputs.items() if k != "token_type_ids"}
+        with torch.no_grad():
+            score = model(**inputs)["score"].item()
+        sentiment = "positive" if score > 0.3 else "negative" if score < -0.3 else "neutral"
+        results.append({"text": text, "score": score, "sentiment": sentiment})
+    return results
+#
+# if __name__ == "__main__":
+#     texts = [
+#         "The stock price soared after the earnings report.",
+#         "The company reported significant losses this quarter.",
+#         "There was no noticeable change in performance."
+#     ]
+#
+#     predictions = predict(texts)
+#     for pred in predictions:
+#         print(f"Text: {pred['text']}")
+#         print(f"Score: {pred['score']:.3f}")
+#         print(f"Sentiment: {pred['sentiment']}\n")