Update README.md

README.md

@@ -9,4 +9,61 @@ library_name: transformers
 tags:
 - 'laptop '
 - t5
----
+---
+
+
+# Laptop-T5-Base
+
+
+The Laptop-T5-Base model was introduced in [A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis (SIGIR'23)](https://doi.org/10.1145/3539618.3591940) by Zengzhi Wang, Qiming Xie, and Rui Xia.
+
+The details are available at [Github:FS-ABSA](https://github.com/nustm/fs-absa) and [SIGIR'23 paper](https://doi.org/10.1145/3539618.3591940).
+
+
+# Model Description
+
+To bridge the domain gap between general pre-training and the task of interest in a specific domain (i.e., `laptop` in this repo), we conducted *domain-adaptive pre-training*, 
+i.e., continuing pre-training the language model (i.e., T5) on the unlabeled corpus of the domain of interest (i.e., `laptop`) with the *text-infilling objective*
+(corruption rate of 15% and average span length of 1). We collect relevant 100k unlabeled reviews from Yelp for the laptop domain, respectively. 
+For pre-training, we employ the [Adafactor](https://arxiv.org/abs/1804.04235) optimizer with a batch size of 80 and a constant learning rate of 1e-4. 
+
+Our model can be seen as an enhanced T5 model in the laptop domain, which can be used for various NLP tasks related to the laptop domain, 
+including but not limited to fine-grained sentiment analysis (ABSA), product-relevant Question Answering (PrQA), text style transfer, etc.
+
+
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+>>> tokenizer = AutoTokenizer.from_pretrained("NUSTM/laptop-t5-base")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("NUSTM/laptop-t5-base")
+
+>>> input_ids = tokenizer(
+...    "The pizza here is delicious!!", return_tensors="pt"
+... ).input_ids  # Batch size 1
+>>> outputs = model(input_ids=input_ids)
+```
+
+
+# Citation
+
+If you find this work helpful, please cite our paper as follows:
+
+```bibtex
+@inproceedings{wang2023fs-absa,
+author = {Wang, Zengzhi and Xie, Qiming and Xia, Rui},
+title = {A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis},
+year = {2023},
+isbn = {9781450394086},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3539618.3591940},
+doi = {10.1145/3539618.3591940},
+booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+numpages = {6},
+location = {Taipei, Taiwan},
+series = {SIGIR '23}
+}
+```
+
+Note that the complete citation format will be announced once our paper is published in the SIGIR 2023 conference proceedings.