Update README.md

README.md

@@ -4,30 +4,34 @@ library_name: transformers
 license: apache-2.0
 ---
 
-# Encoder Model from "Should We Still Pretrain Encoders with Masked Language Modeling?"
+# Overview
 
-This repository contains an encoder model, part of the research presented in the paper **"[Should We Still Pretrain Encoders with Masked Language Modeling?](https://huggingface.co/papers/2507.00994)"**.
-
-This paper investigates the effectiveness of Masked Language Modeling (MLM) versus Causal Language Modeling (CLM) for pretraining text encoders to achieve high-quality text representations. It demonstrates that while MLM generally yields better performance, CLM-trained models are more data-efficient. The research further proposes a biphasic training strategy that sequentially applies CLM and then MLM, achieving optimal performance under a fixed computational budget.
+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.).
 
 *   **Paper:** [Should We Still Pretrain Encoders with Masked Language Modeling?](https://huggingface.co/papers/2507.00994)
-*   **Project Page:** [https://hf.co/MLMvsCLM](https://hf.co/MLMvsCLM)
-*   **Code:** [https://github.com/Nicolas-BZRD/EuroBERT](https://github.com/Nicolas-BZRD/EuroBERT)
-
-## Model Description
-
-This model is an encoder designed to produce robust text representations for a wide range of natural language processing tasks. It is trained as part of an extensive study on encoder pretraining objectives, focusing on the trade-offs and benefits of MLM and CLM, and the effectiveness of a biphasic training approach. The model architecture is an `SLModel` as identified in the `config.json`.
+*   **Blog post:** [Link](https://huggingface.co/blog/Nicolas-BZRD/encoders-should-not-be-only-pre-trained-with-mlm)
+*   **Project page:** [https://hf.co/MLMvsCLM](https://hf.co/MLMvsCLM)
+
+## Model Naming
+
+Model identifiers follow a consistent format that encodes key training details:
+
+* **Single-stage models**:
+`[model size]-[objective]-[number of steps]`.
+Example: `610m-clm-42k` denotes a 610M-parameter model trained with CLM for 42,000 steps.
+* **Two-stage models**:
+`[model size]-[objective #1]-[steps #1]-[objective #2]-[total steps]`.
+Example: `610m-clm-10k-mlm40-42k` indicates a 610M model trained first with CLM for 10k steps, then continued with MLM (40% masking ratio) for 32k more steps, totaling 42k steps.
+* **Continued pretraining from decayed checkpoints**:
+These use the dec prefix on the first training stage.
+Example: `610m-clm-dec42k-mlm40-64k refers` to a 610M model pretrained with CLM for 42k steps (with weight decay), then further trained with MLM (40% masking) for 22k additional steps, totaling 64k.
+* **Intermediate checkpoints**:
+To refer to a specific training step before the final checkpoint, append the step number at the end.
+Example: `610m-mlm40-42k-1000` corresponds to step 1,000 during the MLM training phase of a 610M model trained for 42k steps.
 
 ## Usage
 
-You can use this model for feature extraction with the Hugging Face `transformers` library. Since this model might use a custom architecture (`SLModel`), you may need to install the associated `EuroBERT` package and use `trust_remote_code=True` when loading the model.
-
-First, install the `EuroBERT` package:
-```bash
-pip install git+https://github.com/Nicolas-BZRD/EuroBERT.git
-```
-
-Then, you can load and use the model as follows:
+You can use this model for feature extraction with the Hugging Face `transformers` library.
 
 ```python
 from transformers import AutoTokenizer, AutoModel
@@ -62,4 +66,20 @@ sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)
 sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 mean_pooled_embedding = sum_embeddings / sum_mask
 print(f"Shape of mean pooled embedding: {mean_pooled_embedding.shape}")
+```
+
+## Citation
+
+If you found this model useful, please consider citing our paper:
+
+```bibtex
+@misc{gisserotboukhlef2025pretrainencodersmaskedlanguage,
+      title={Should We Still Pretrain Encoders with Masked Language Modeling?}, 
+      author={Hippolyte Gisserot-Boukhlef and Nicolas Boizard and Manuel Faysse and Duarte M. Alves and Emmanuel Malherbe and André F. T. Martins and Céline Hudelot and Pierre Colombo},
+      year={2025},
+      eprint={2507.00994},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.00994}, 
+}
 ```