Instructions to use MLMvsCLM/1b-mlm20-42k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLMvsCLM/1b-mlm20-42k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MLMvsCLM/1b-mlm20-42k", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLMvsCLM/1b-mlm20-42k", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add link to GitHub repository
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by nielsr HF Staff - opened
README.md
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pipeline_tag: feature-extraction
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library_name: transformers
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license: apache-2.0
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---
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# Overview
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This repository contains an encoder model, part of the research presented in the paper *Should We Still Pretrain Encoders with Masked Language Modeling?* (Gisserot-Boukhlef et al.).
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* **Paper:** [Should We Still Pretrain Encoders with Masked Language Modeling?](https://huggingface.co/papers/2507.00994)
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* **Blog post:** [Link](https://huggingface.co/blog/Nicolas-BZRD/encoders-should-not-be-only-pre-trained-with-mlm)
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* **Project page:** [https://hf.co/MLMvsCLM](https://hf.co/MLMvsCLM)
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---
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library_name: transformers
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license: apache-2.0
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pipeline_tag: feature-extraction
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---
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# Overview
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This repository contains an encoder model, part of the research presented in the paper *Should We Still Pretrain Encoders with Masked Language Modeling?* (Gisserot-Boukhlef et al.).
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* **Paper:** [Should We Still Pretrain Encoders with Masked Language Modeling?](https://huggingface.co/papers/2507.00994)
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* **Code:** [https://github.com/Nicolas-BZRD/EuroBERT](https://github.com/Nicolas-BZRD/EuroBERT)
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* **Blog post:** [Link](https://huggingface.co/blog/Nicolas-BZRD/encoders-should-not-be-only-pre-trained-with-mlm)
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* **Project page:** [https://hf.co/MLMvsCLM](https://hf.co/MLMvsCLM)
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