Add comprehensive model card for encoder model

README.md

@@ -0,0 +1,65 @@
+---
+pipeline_tag: feature-extraction
+library_name: transformers
+license: apache-2.0
+---
+
+# Encoder Model from "Should We Still Pretrain Encoders with Masked Language Modeling?"
+
+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.
+
+*   **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`.
+
+## 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:
+
+```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}")
+```