Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+#Encoder from HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding.
+
+## Model Details
+
+#Encoder was pre-trained on 179M Twitter posts, each containing a hashtag. 
+It was based on pairwise posts, and contrastive learning guided them to learn topic relevance via learning to identify posts with the same hashtag. 
+We randomly noise the hashtags to avoid trivial representation.
+Please refers to https://github.com/albertan017/HICL for more details.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Hanzhuo Tan, Department of Computing, the Hong Kong Polytechnic University
+- **Model type:** Roberta
+- **Language(s) (NLP):** English
+- **License:** n.a
+- **Finetuned from model [optional]:** Bertweet
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/albertan017/HICL
+- **Paper [optional]:** HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+```
+from transformers import AutoModel, AutoTokenizer 
+
+hashencoder = AutoModel.from_pretrained("albertan017/hashencoder")
+
+tokenizer = AutoTokenizer.from_pretrained("albertan017/hashencoder")
+
+tweet = "here's a sample tweet for encoding"
+
+input_ids = torch.tensor([tokenizer.encode(tweet)])
+
+with torch.no_grad():
+    features = hashencoder(input_ids)  # Models outputs are now tuples
+
+```
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+We do not inforce semantic similarity.
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+N.A.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+
+