metga97
/

functiongemma-270m-ar-tooluse

@@ -3,197 +3,89 @@ library_name: transformers
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 tags: []
 ---
+language:
+- ar
+tags:
+- function-calling
+- tool-use
+- arabic
+- instruction-tuning
+- gemma
+- transformers
+license: apache-2.0
+base_model: google/functiongemma-270m-it
+---
+# FunctionGemma-270M Arabic Tool Use
+This model is a finetuned version of **`google/functiongemma-270m-it`** for Arabic **tool use / function calling** across multiple dialects and domains.
+It is trained to produce **exactly one tool call** when a tool is required, using **FunctionGemma-native tool formatting** (special function-call tokens) and structured JSON arguments.
+## Base model
+- `google/functiongemma-270m-it`
+## Dataset
+- `metga97/arabic-tooluse-functiongemma-v1` (private or public depending on your repo settings)
+## What the model outputs
+When a tool is required, generation should include a FunctionGemma tool call pattern such as:
+- `<start_function_call>call:TOOL_NAME{ ...json args... }<end_function_call>`
+For non-tool requests, it returns a short Arabic reply.
+## Evaluation (by slang / dialect)
+Evaluated on the test split of `metga97/arabic-tooluse-functiongemma-v1`.
+### Overall
+- Parsed OK rate: **0.891**
+- Tool name accuracy: **0.9921**
+- Strict EM: **0.6564**
+- Key-F1 (avg): **0.9925**
+- Missed-call rate: **0.0064**
+- False-call rate (negatives): **0.0**
+### Strict EM by slang / dialect
+- **Egyptian**: 0.6791 (denom_calls: 1069)
+- **Gulf**: 0.6237 (denom_calls: 1172)
+- **Levantine**: 0.6558 (denom_calls: 706)
+- **MSA**: 0.6804 (denom_calls: 1408)
+- **Maghrebi**: 0.5455 (denom_calls: 176)
+### Strict EM by domain
+- banking_finance: 0.6255 (denom_calls: 542)
+- ecommerce: 0.64 (denom_calls: 550)
+- government_services: 0.7651 (denom_calls: 613)
+- healthcare: 0.5754 (denom_calls: 577)
+- islamic_services: 0.7119 (denom_calls: 597)
+- travel: 0.6028 (denom_calls: 564)
+- utilities: 0.4652 (denom_calls: 561)
+- weather: 0.8653 (denom_calls: 527)
+## Inference (important)
+### 1) Use left padding for decoder-only generation
+Set:
+- `tokenizer.padding_side = "left"`
+- `tokenizer.pad_token = tokenizer.eos_token` (if missing)
+### 2) Pass tools via `apply_chat_template(..., tools=tools_list)`
+This is critical for FunctionGemma-style function calling.
+Example outline:
+1. Select a tool subset for the request (domain pack + deterministic sampling).
+2. Build prompt with `apply_chat_template` including `tools=tools_list`.
+3. `generate()` deterministically (`do_sample=False`, `temperature=0.0`).
+4. Parse tool call tokens and arguments.
+## Known limitations / improvement ideas
+- Some outputs may translate slot values into English (e.g., “Abu Dhabi”, “ID renewal”).
+  - Mitigations: stronger developer prompt constraints, post-processing, adding explicit anti-translation supervision, and/or filtering/rebalancing training examples where values are English.
+- Parsed OK < 1.0: you can improve formatting consistency with:
+  - longer training
+  - slightly stronger prompt
+  - adding more negative/no-tool examples with explicit non-tool responses