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Model: bratao/Qwen3OIE-0.6B Source: Original Platform
2026-10-09 21:04:10 +08:00
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--- a/README.md
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+---
+language:
+- pt
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-0.6B
+license: apache-2.0
+tags:
+- open-information-extraction
+- openie
+- portuguese
+- abstractive-openie
+- qwen3
+---
+
+# Qwen3OIE-0.6B
+
+Qwen3OIE-0.6B is a Portuguese **abstractive Open Information Extraction (OpenIE)**
+model fine-tuned from [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
+Given one sentence, it generates one or more binary extractions with the fields
+`ARG0`, `V`, and `ARG1` in JSON.
+
+This is the smallest published Qwen3OIE checkpoint and the recommended first model
+for local experiments. It may normalize or infer wording instead of copying every
+span literally from the source; applications that require strict provenance should
+validate every generated field against the input or use an extractive model.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Public repository | [`bratao/Qwen3OIE-0.6B`](https://huggingface.co/bratao/Qwen3OIE-0.6B) |
+| Base model | [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) |
+| Architecture | decoder-only causal language model |
+| Task | Portuguese abstractive OpenIE |
+| Parameters | 596,049,920 |
+| Published weight precision | bfloat16 |
+| Approximate repository size | 1.21 GB |
+| Audited revision | `140ed13943b107f281600ddc2d3caa37f4a4d062` (2026-08-30) |
+
+The thesis reports this model as “Qwen 0.5B”, following the label used during the
+experiments. The public upstream checkpoint and this repository are named 0.6B; the
+parameter count above comes from the published configuration.
+
+## Use with `portuguese-openie`
+
+```bash
+pip install "portuguese-openie[transformers]"
+```
+
+```python
+from portuguese_openie import Model, PortugueseOpenIE
+
+extractor = PortugueseOpenIE(Model.QWEN3_OIE_0_6B)
+triples = extractor.extract("A UFBA está localizada em Salvador.")
+print([triple.to_dict() for triple in triples])
+```
+
+No model path is required. On first use, the library downloads the public model
+files from Hugging Face and stores them in the standard local Hugging Face cache;
+later runs reuse that cache.
+
+Validated output at the audited revision:
+
+```python
+[{"ARG0": "A UFBA", "V": "está localizada em", "ARG1": "Salvador"}]
+```
+
+The end-to-end validation used Python 3.12.9, PyTorch 2.13, Transformers 4.57.6,
+and Accelerate 1.14 on CPU. Model loading took about 8.5 seconds, generation plus
+parsing about 4.1 seconds, and peak process RSS was about 1.58 GB. These are a
+single-machine smoke test, not a benchmark.
+
+## Direct Transformers use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "bratao/Qwen3OIE-0.6B"
+revision = "140ed13943b107f281600ddc2d3caa37f4a4d062"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    revision=revision,
+    dtype="auto",
+    device_map="auto",
+)
+
+sentence = "A UFBA está localizada em Salvador."
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "Dada uma frase S você consegue fazer extrações em JSON no formato "
+            "ARG0 , V, ARG1. Realize a extração para a frase abaixo:"
+        ),
+    },
+    {"role": "user", "content": f"S: {sentence}"},
+]
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False,
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+generated = output[0, inputs["input_ids"].shape[-1]:]
+print(tokenizer.decode(generated, skip_special_tokens=True))
+```
+
+Keep the system prompt, `S:` prefix, chat template, and `enable_thinking=False`.
+The fine-tuning configuration used a sequence length of 2,048 tokens. Longer task
+contexts, even if accepted by the base architecture, were not evaluated here.
+
+## Evaluation
+
+The thesis evaluates the public model family on 100 Portuguese test sentences with
+238 reference extractions from WikiPUD-Portuguese-Abstractive. The reference set was
+generated with an LLM from OIEC-PT Gold source sentences and manually spot-checked;
+it is therefore described as **silver-standard**, not fully human-authored gold data.
+
+| Criterion | Precision | Recall | F1 |
+|---|---:|---:|---:|
+| Perfect match | 0.1136 | 0.1723 | 0.1369 |
+| Lexical match | 0.2493 | 0.3782 | 0.3005 |
+
+Perfect match requires an exact triple match. Lexical match gives partial credit for
+token overlap. Precision and recall come from the associated local evaluation
+summary; the F1 values are also reported in the thesis. These are research results
+on one small dataset, not general Portuguese language guarantees.
+
+## Training-data provenance
+
+The abstractive OpenIE training corpus described in the thesis contains 29,026
+Portuguese sentences and 102,788 synthetic extractions derived from 2,015 Portuguese
+Wikipedia paragraphs with Gemini 2.5 Flash. The public model repository does not
+declare a Hugging Face dataset identifier and does not include that training corpus;
+accordingly, this card deliberately omits a `datasets` field.
+
+## Requirements and hardware
+
+- Python 3.10+ with recent `torch`, `transformers`, and `accelerate`.
+- The bfloat16 weights occupy about 1.2 GB. Around 4 GB of system RAM is a practical
+  starting point for CPU inference; GPU execution is optional.
+- Actual memory and latency depend on sequence length, software versions, and device.
+
+## Limitations and responsible use
+
+- Generative OpenIE can omit relations, duplicate extractions, hallucinate content,
+  or emit malformed JSON. Always parse defensively and retain the source sentence.
+- The model was evaluated on only 100 mostly encyclopedic Portuguese sentences.
+- It may perform poorly on dialectal, conversational, specialized, very long, or
+  adversarial text and has not been audited for demographic bias.
+- An extraction is not a verified fact. Do not use it as the sole basis for medical,
+  legal, financial, or other high-impact decisions.
+
+## License
+
+This repository declares the Apache License 2.0. Use also remains subject to the
+terms of the upstream Qwen model and to any applicable rights in input or training
+data. The training dataset itself is not distributed by this model card.
+
+## Citation
+
+```bibtex
+@phdthesis{cabral2025evolving,
+  author = {Cabral, Bruno Souza},
+  title = {Evolving Open Information Extraction for Portuguese employing Language Models},
+  school = {Universidade Federal da Bahia},
+  year = {2025}
+}
+
+@inproceedings{cabral2022portnoie,
+  author = {Cabral, Bruno and Souza, Marlo and Claro, Daniela Barreiro},
+  title = {PortNOIE: A Neural Framework for Open Information Extraction for the Portuguese Language},
+  booktitle = {Computational Processing of the Portuguese Language (PROPOR 2022)},
+  year = {2022},
+  doi = {10.1007/978-3-030-98305-5_23}
+}
+```
+
+Project: [Portuguese-OpenIE](https://github.com/FORMAS/Portuguese-OpenIE) ·
+[PortNOIE paper](https://doi.org/10.1007/978-3-030-98305-5_23) ·
+[Generative OpenIE paper](https://aclanthology.org/2024.propor-1.13/)
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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
--- a/vocab.json
+++ b/vocab.json