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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- financial-sentiment
+- sentiment-analysis
+- finance
+- nlp
+- transformers
+datasets:
+- zeroshot/twitter-financial-news-sentiment
+metrics:
+- accuracy
+- f1
+model-index:
+- name: financial-sentiment-bert-large
+  results:
+  - task:
+      type: text-classification
+      name: Financial Sentiment Analysis
+    dataset:
+      name: Twitter Financial News Sentiment
+      type: zeroshot/twitter-financial-news-sentiment
+    metrics:
+    - type: accuracy
+      value: 0.843
+      name: Accuracy
+---
+
+# financial-sentiment-bert-large
+
+## Model Description
+
+BERT-Large financial sentiment analysis model with high accuracy
+
+This model is fine-tuned from `bert-large-uncased` for financial sentiment analysis, capable of classifying financial text into three categories:
+- **Bearish** (0): Negative financial sentiment
+- **Neutral** (1): Neutral financial sentiment  
+- **Bullish** (2): Positive financial sentiment
+
+## Model Performance
+
+- **Accuracy**: 0.843
+- **Dataset**: Twitter Financial News Sentiment
+- **Base Model**: bert-large-uncased
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("codealchemist01/financial-sentiment-bert-large")
+model = AutoModelForSequenceClassification.from_pretrained("codealchemist01/financial-sentiment-bert-large")
+
+# Example usage
+text = "Apple stock is showing strong growth potential"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+
+# Labels: 0=Bearish, 1=Neutral, 2=Bullish
+labels = ["Bearish", "Neutral", "Bullish"]
+print(f"Prediction: {labels[predicted_class]}")
+```
+
+## Training Details
+
+- **Training Dataset**: Twitter Financial News Sentiment
+- **Training Framework**: Transformers
+- **Optimization**: AdamW
+- **Hardware**: RTX GPU
+
+## Limitations
+
+This model is specifically trained for financial sentiment analysis and may not perform well on general sentiment analysis tasks.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{financial-sentiment-large,
+  author = {CodeAlchemist01},
+  title = {financial-sentiment-bert-large},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/codealchemist01/financial-sentiment-bert-large}
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b81e99de564442003eb9699260ba4b22ba103d0daf409427a46d46f0566d686d
+size 1340626860

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}