numind
/

NuExtract-tiny-v1.5

@@ -1,199 +1,94 @@
 ---
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: mit
+language:
+- multilingual
+tags:
+- nlp
+base_model: Qwen/Qwen2.5-0.5B
+---
+# NuExtract-tiny-v1.5 by NuMind 🔥
+NuExtract-v1.5 is a fine-tuning of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B), trained on a private high-quality dataset for structured information extraction. It supports long documents and several languages (English, French, Spanish, German, Portuguese, and Italian).
+To use the model, provide an input text and a JSON template describing the information you need to extract.
+Note: This model is trained to prioritize pure extraction, so in most cases all text generated by the model is present as is in the original text.
+Try it here: <space-link>
+We also provide a 3.8B version which is based on Phi-3.5-mini-instruct: [NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5)
+**Checkout other models by NuMind:**
+- NuExtract Version 1 Models: [0.5B](https://huggingface.co/numind/NuExtract-tiny), [3.8B](https://huggingface.co/numind/NuExtract), [7B](https://huggingface.co/numind/NuExtract-large)
+- SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
+- SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
+- SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
+## Benchmark
+Include result plots.
+## Usage
+To use the model:
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+def predict_NuExtract(model, tokenizer, texts, template, batch_size=1, max_length=10_000, max_new_tokens=4_000):
+    template = json.dumps(json.loads(template), indent=4)
+    prompts = [f"""<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>""" for text in texts]
+    outputs = []
+    with torch.no_grad():
+        for i in range(0, len(prompts), batch_size):
+            batch_prompts = prompts[i:i+batch_size]
+            batch_encodings = tokenizer(batch_prompts, return_tensors="pt", truncation=True, padding=True, max_length=max_length).to(model.device)
+            pred_ids = model.generate(**batch_encodings, max_new_tokens=max_new_tokens)
+            outputs += tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    return [output.split("<|output|>")[1] for output in outputs]
+model_name = "numind/NuExtract-v1.5"
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device).eval()
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
+superior performance and efficiency. Mistral 7B outperforms the best open 13B
+model (Llama 2) across all evaluated benchmarks, and the best released 34B
+model (Llama 1) in reasoning, mathematics, and code generation. Our model
+leverages grouped-query attention (GQA) for faster inference, coupled with sliding
+window attention (SWA) to effectively handle sequences of arbitrary length with a
+reduced inference cost. We also provide a model fine-tuned to follow instructions,
+Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
+automated benchmarks. Our models are released under the Apache 2.0 license.
+Code: <https://github.com/mistralai/mistral-src>
+Webpage: <https://mistral.ai/news/announcing-mistral-7b/>"""
+template = """{
+    "Model": {
+        "Name": "",
+        "Number of parameters": "",
+        "Number of max token": "",
+        "Architecture": []
+    },
+    "Usage": {
+        "Use case": [],
+        "Licence": ""
+    }
+}"""
+prediction = predict_NuExtract(model, tokenizer, [text], template)[0]
+print(prediction)
+```