Update README.md

README.md

@@ -1,3 +1,91 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- recommendation
+- collaborative filtering
+---
+
+# EasyRec-Small
+
+## Overview
+
+- **Description**: EasyRec is a series of language models designed for recommendations, trained to match the textual profiles of users and items with collaborative signals.
+- **Usage**: You can use EasyRec to encode user and item text embeddings based on the textual profiles that reflect their preferences for various recommendation scenarios.
+- **Evaluation**: We evaluate the performance of EasyRec in: (i) Text-based zero-shot recommendation and (ii) Text-enhanced collaborative filtering.
+- **Finetuned from model:** EasyRec is finetuned from [RoBERTa](https://huggingface.co/FacebookAI/roberta-large) within English.
+
+For details please refer to our [💻[GitHub Code](https://github.com/HKUDS/EasyRec)] and [📖[Paper]()].
+
+## Get Started
+
+### Environment
+
+Please run the following  commands to create a conda environment:
+
+```bash
+conda create -y -n easyrec python=3.11
+pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1
+pip install -U "transformers==4.40.0" --upgrade
+pip install accelerate==0.28.0
+pip install tqdm
+pip install sentencepiece==0.2.0
+pip install scipy==1.9.3
+pip install setproctitle
+pip install sentence_transformers
+```
+
+### Example Codes
+Please first download the codes.
+```ssh
+git clone https://github.com/HKUDS/EasyRec.git
+cd EasyRec
+```
+
+Here is an example code snippet to utilize EasyRec for encoding **text embeddings** based on user and item profiles for recommendations.
+
+```Python
+import torch
+from model import Easyrec
+import torch.nn.functional as F
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+
+config = AutoConfig.from_pretrained("hkuds/easyrec-roberta-small")
+model = Easyrec.from_pretrained("hkuds/easyrec-roberta-small", config=config,)
+tokenizer = AutoTokenizer.from_pretrained("hkuds/easyrec-roberta-small", use_fast=False,)
+
+profiles = [
+    'This user is a basketball fan and likes to play basketball and watch NBA games.', # user
+    'This basketball draws in NBA enthusiasts.', # item 1
+    'This item is nice for swimming lovers.'     # item 2
+]
+
+inputs = tokenizer(profiles, padding=True, truncation=True, max_length=512, return_tensors="pt")
+with torch.inference_mode():
+    embeddings = model.encode(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)
+embeddings = F.normalize(embeddings.pooler_output.detach().float(), dim=-1)
+
+print(embeddings[0] @ embeddings[1])    # 0.9260
+print(embeddings[0] @ embeddings[2])    # 0.5834
+```
+
+### Model List
+We release a series of EasyRec checkpoints with varying sizes. You can easily load these models from Hugging Face by replacing the model name.
+|              Model              | Model Size | Recall@20 on Amazon-Sports |
+|:-------------------------------|:--------:| :--------:|
+| [hkuds/easyrec-roberta-small](https://huggingface.co/hkuds/easyrec-roberta-small) |  82M  | 0.0286 |
+| [hkuds/easyrec-roberta-base](https://huggingface.co/hkuds/easyrec-roberta-base)   |  125M  | 0.0518  |
+| [hkuds/easyrec-roberta-large](https://huggingface.co/hkuds/easyrec-roberta-large) |  355M  | 0.0557  |
+
+## 🌟 Citation
+If you find this work is helpful to your research, please consider citing our paper:
+```bibtex
+@inproceedings{ren2024easyrec,
+  title={EasyRec: Simple yet Effective Language Models for Recommendation},
+  author={Ren, Xubin and Huang, Chao},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+**Thanks for your interest in our work!**