Create README.md

README.md

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+---
+license: cc
+language:
+- en
+---
+This is the proposition segmentation model from "Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations" by Chen et. al. 2023. 
+
+## Usage
+The prompt to the model is formatted like: `segment sentence: {input_sentence}`. 
+
+For each sentence, the model will output the propositions concatenated by `[sep]` as a string.  
+
+For example, if we use the following example code to segment `"Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist."`. 
+
+The model output will be `['Dracula is a novel by Bram Stoker.[sep]Count Dracula is the protagonist of Dracula.']`
+
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+gen_kwargs = {
+    "length_penalty": 0,
+    "max_new_tokens": 256,
+    "min_length": 10,
+    "no_repeat_ngram_size": 0,
+    "num_beams": 1,
+}
+
+SEGMENT5_PROMPT = "segment sentence: {}"
+SEGMENT5_SEP_TOKEN = "[sep]"
+
+model = AutoModelForSeq2SeqLM.from_pretrained("sihaochen/SegmenT5-large")
+tokenizer = AutoTokenizer.from_pretrained("sihaochen/SegmenT5-large")
+
+model.eval()
+
+# define an example input sentence
+example_sentence = "Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist."
+example_input = SEGMENT5_PROMPT.format(example_sentence)
+
+input_ids = tokenizer(example_input,
+                      return_tensors="pt",
+                      padding="max_length",
+                      max_length=512,
+                      truncation=True).input_ids
+
+logits = model.generate(input_ids, **gen_kwargs)
+outputs = tokenizer.batch_decode(logits, skip_special_tokens=True)
+
+
+output = outputs[0].split(SEGMENT5_SEP_TOKEN)
+
+print(output)
+# Output: ['Dracula is a novel by Bram Stoker.', 'Count Dracula is the protagonist of Dracula.']
+```
+
+## Sub-Sentence Encoder
+For model checkpoints + code for the sub-sentence encoders, checkout: https://github.com/schen149/sub-sentence-encoder/
+
+## Citation 
+```
+@article{chen2023subsentence,
+  title={Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations},
+  author={Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu},
+  journal={arXiv preprint arXiv:2311.04335},
+  year={2023},
+  URL = {https://arxiv.org/pdf/2311.04335.pdf}
+}
+```
+