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--- |
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inference: false |
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language: pt |
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datasets: |
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- assin2 |
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--- |
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# BERTimbau base for Semantic Textual Similarity |
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This is the [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for |
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Semantic Textual Similarity with the [ASSIN 2](https://huggingface.co/datasets/assin2) dataset. |
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This model is suitable for Portuguese. |
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- Git Repo: [Evaluation of Portuguese Language Models](https://github.com/ruanchaves/eplm). |
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- Demo: [Portuguese Semantic Similarity](https://ruanchaves-portuguese-semantic-similarity.hf.space) |
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## Full regression example |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig |
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import numpy as np |
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import torch |
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model_name = "ruanchaves/bert-base-portuguese-cased-assin2-similarity" |
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s1 = "A gente faz o aporte financeiro, é como se a empresa fosse parceira do Monte Cristo." |
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s2 = "Fernando Moraes afirma que não tem vínculo com o Monte Cristo além da parceira." |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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config = AutoConfig.from_pretrained(model_name) |
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model_input = tokenizer(*([s1], [s2]), padding=True, return_tensors="pt") |
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with torch.no_grad(): |
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output = model(**model_input) |
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score = output[0][0].detach().numpy().item() |
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print(f"Similarity Score: {np.round(float(score), 4)}") |
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``` |
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## Citation |
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Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon. |
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In the meanwhile, if you would like to cite our work or models before the publication of the paper, please cite our [GitHub repository](https://github.com/ruanchaves/eplm): |
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``` |
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@software{Chaves_Rodrigues_eplm_2023, |
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author = {Chaves Rodrigues, Ruan and Tanti, Marc and Agerri, Rodrigo}, |
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doi = {10.5281/zenodo.7781848}, |
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month = {3}, |
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title = {{Evaluation of Portuguese Language Models}}, |
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url = {https://github.com/ruanchaves/eplm}, |
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version = {1.0.0}, |
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year = {2023} |
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} |
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``` |
