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README.md

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+---
+language: en
+license: mit
+tags:
+- deepseek
+- financial
+- sentiment-analysis
+- financial-news
+- stock-market
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+---
+
+# FinBot - Financial Sentiment Analyzer
+
+This model fine-tunes the DeepSeek-R1-Distill-Qwen-1.5B model to analyze sentiment in financial news and reports. It classifies financial news into Bullish, Bearish, or Neutral sentiment categories based on the implied impact on specific stocks.
+
+## Model Description
+
+- **Base Model**: [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
+- **Task**: Financial sentiment analysis
+- **Training Technique**: LoRA fine-tuning
+- **Target Domain**: Financial news, stock market reports, and company announcements
+
+## Intended Uses
+
+This model is designed to help investors, traders, and financial analysts quickly assess the sentiment implications of financial news for specific stocks. It can be used for:
+
+- Analyzing news sentiment for portfolio management
+- Screening large volumes of financial news
+- Generating sentiment signals for trading strategies
+- Research on market sentiment and stock price correlations
+
+## Usage
+
+You can use this model to analyze the sentiment of financial news by providing a news title, summary, and the stock ticker:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import json
+
+# Load model and tokenizer
+model_name = "jialeCharlotte/finbot"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+def analyze_sentiment(news_title, news_summary, ticker):
+    prompt = f"""You are a financial analyst in a leading hedge fund. 
+Analyze the sentiment of the following financial news for the given stock ticker step by step.
+
+Title: "{news_title}"
+Summary: "{news_summary}"
+Stock Ticker: {ticker}
+
+Step 1: Identify key financial terms and their implications.
+Step 2: Determine whether the news suggests market optimism, pessimism, or neutrality for this specific stock.
+Step 3: Based on your analysis, classify the sentiment into one of the following categories:
+- "Bullish": If the news suggests confidence, growth, or positive impact on this stock.
+- "Bearish": If the news suggests decline, risks, or negative impact on this stock.
+- "Neutral": If the news is ambiguous or does not convey strong sentiment.
+
+Finally, **return only** the final result in valid JSON format, with the structure:
+{{
+  "ticker": "{ticker}",
+  "sentiment": "Bullish" | "Bearish" | "Neutral",
+  "sentiment_reasoning": "Provide a brief explanation of the sentiment analysis."
+}}
+
+Do not include any extra text or explanations outside the JSON.
+### Response:
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            pad_token_id=tokenizer.pad_token_id
+        )
+    
+    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    
+    try:
+        # Parse the JSON response
+        result = json.loads(response)
+        return result
+    except json.JSONDecodeError:
+        # If the response isn't valid JSON, return the raw text
+        return {"error": "Failed to parse response", "raw_response": response}
+
+# Example usage
+news_title = "Apple Reports Record Q1 Revenue"
+news_summary = "Apple Inc. announced today that they have achieved record-breaking revenue in Q1 2025, exceeding analyst expectations by 15%."
+ticker = "AAPL"
+
+result = analyze_sentiment(news_title, news_summary, ticker)
+print(result)
+```
+
+## Training Data
+
+The model was trained on a curated dataset of financial news articles and reports, each labeled with sentiment classifications (Bullish, Bearish, or Neutral) specific to the mentioned stock tickers. The training data includes diverse sources of financial information covering various market sectors and company types.
+
+## Limitations
+
+- The model is focused on English-language financial content
+- Performance may vary for highly technical financial documents or specialized industry sectors
+- The model analyzes sentiment specifically for the mentioned ticker, not overall market sentiment
+- As with all language models, outputs should be reviewed by human experts for critical financial decisions
+
+## Ethical Considerations
+
+This model is intended to assist with financial analysis but should not be the sole basis for investment decisions. Users should:
+
+- Always verify model outputs against other sources
+- Be aware that financial markets are influenced by many factors beyond sentiment
+- Consider the model's outputs as one of many inputs in a comprehensive analysis process
+- Not use the model for market manipulation or other unethical financial activities
+
+## Citation
+
+If you use this model in your research or application, please cite:
+
+```
+@misc{finbot2025,
+  author = {Charlotte Zhou, Zhilin Zhu},
+  title = {FinBot - Financial Sentiment Analyzer},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/jialeCharlotte/finbot}}
+}
+```

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+size 4372840

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<｜end▁of▁sentence｜>"
+}

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151645": {
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+    },
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+      "normalized": false,
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+      "special": false
+    }
+  },
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin��>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizerFast",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}

training_args.bin

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