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README.md

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+---
+pipeline_tag: feature-extraction
+library_name: transformers
+license: apache-2.0
+---
+
+# Overview
+
+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)
+*   **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.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Replace with the actual model ID if different, e.g., "AhmedAliHassan/MLMvsCLM-Biphasic-210M"
+# This placeholder assumes the current repository is the model you want to load.
+model_name = "<YOUR_MODEL_ID_HERE>" 
+
+# Load the tokenizer and model, ensuring trust_remote_code for custom architectures
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+
+text = "This is an example sentence to extract features from."
+
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# The last hidden state contains the token embeddings (features)
+last_hidden_state = outputs.last_hidden_state
+print(f"Shape of last hidden state: {last_hidden_state.shape}")
+
+# For sentence-level embeddings, common approaches include:
+# 1. Averaging the token embeddings (excluding special tokens)
+# 2. Using the embedding of the [CLS] token (if applicable for the model's architecture)
+# Example: Mean pooling (simple average over non-padding tokens)
+attention_mask = inputs["attention_mask"]
+input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
+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}, 
+}
+```