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Model: tegana/qwen2.5-arabic-finance-news-parser Source: Original Platform
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README.md
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README.md
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---
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language:
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- ar
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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tags:
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- fine-tuned
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- arabic
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- financial-nlp
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- information-extraction
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- lora
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- llama-factory
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datasets:
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- custom
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pipeline_tag: text-generation
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---
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# qwen2.5-arabic-finance-news-parser
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A fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for **structured information extraction from Arabic financial news articles**.
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## Model Description
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This model was fine-tuned using **LoRA** via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) on a dataset of ~2,792 Egyptian stock-market news articles. Given a news article and a JSON output schema, the model extracts structured data such as company name, event type, sentiment, financial figures, and a short summary.
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## Training Details
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| Setting | Value |
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|---|---|
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| Base model | Qwen/Qwen2.5-1.5B-Instruct |
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| Fine-tuning method | LoRA (rank 64, all targets) |
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| Dataset size | 2,792 samples (2,700 train / 92 val) |
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| Epochs | 3 |
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| Learning rate | 1e-4 (cosine scheduler) |
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| Max sequence length | 3,500 tokens |
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| Hardware | Kaggle T4 GPU |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch, json
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model_id = "tegana/qwen2.5-arabic-finance-news-parser"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id, device_map="auto", torch_dtype=torch.bfloat16
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)
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article = "القاهرة - واصل جهاز مستقبل مصر للتنمية المستدامة..."
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output_scheme = json.dumps({
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"company_name": "اسم الشركة",
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"event_type": "acquisition|earnings|dividends|...",
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"sentiment": "positive|negative|neutral",
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"impact_level": "high|medium|low",
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"short_summary": "ملخص من 3 إلى 5 جمل"
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}, ensure_ascii=False)
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messages = [
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{"role": "system", "content": (
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"You are a professional Arabic financial news parser.\n"
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"Extract structured information and return ONLY a valid JSON object."
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)},
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{"role": "user", "content": f"## Article:\n{article}\n\n## Output Scheme:\n{output_scheme}\n\n## Output JSON:"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(inputs.input_ids, max_new_tokens=512, do_sample=False)
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out_ids = [o[len(i):] for i, o in zip(inputs.input_ids, out)]
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print(tokenizer.batch_decode(out_ids, skip_special_tokens=True)[0])
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```
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## Supported Event Types
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`earnings` · `capital_increase` · `capital_decrease` · `dividends` · `acquisition` · `sale_of_stake` · `financing` · `project` · `board_decision` · `regulatory_approval` · `analysis_financial` · `stock_exchange_decision` · `other`
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## Limitations
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- Trained primarily on Egyptian stock-market news; may underperform on other Arabic financial dialects.
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- Numerical extraction quality depends on how clearly figures appear in the source text.
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## License
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Apache 2.0 — same as the base model.
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.2.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.2.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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tokenizer_config.json
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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