Model card

README.md

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+---
+language: 
+  - en
+tags:
+- hupd
+- t5
+- summarization
+- conditional-generation
+- patents
+license: cc-by-sa-4.0
+datasets:
+- HUPD/hupd
+---
+
+# HUPD T5-Small Summarization Model
+
+This HUPD T5-Small summarization model was fine-tuned on the HUPD dataset. It was originally introduced in [this paper](TBD). 
+
+For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentdataset.org/) or the [project's GitHub repository](https://github.com/suzgunmirac/hupd).
+
+
+### How to Use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+from transformers import pipeline
+summarizer = pipeline(task="summarization", model="turingmachine/hupd-t5-small")
+
+TEXT = "1. An optical coherent receiver for an optical communication network, said optical coherent receiver being configured to receive a modulated optical signal and to process said modulated optical signal for generating an in-phase component and a quadrature component, said in-phase component and said quadrature component being electrical signals, said optical coherent receiver comprising a power adjuster in turn comprising: a multiplying unit configured to multiply said in-phase component by an in-phase gain thereby providing a power-adjusted in-phase component, and to multiply said quadrature component by a quadrature gain thereby providing a power-adjusted quadrature component; and a digital circuit connected between output and input of said multiplying unit and configured to compute: a common gain indicative of a sum of a power of said power-adjusted in-phase component and a power of said power-adjusted quadrature component, and a differential gain indicative of a difference between said power of said power-adjusted in-phase component and said power of said power-adjusted quadrature component; and said in-phase gain as a product between said common gain and said differential gain, and said quadrature gain as a ratio between said common gain and said differential gain. 2. An optical coherent receiver according to claim 1, wherein it further comprises an analog-to-digital unit connected at the input of said power adjuster, said analog-to-digital unit being configured to ..."
+
+summarizer(TEXT)
+```
+
+Here is the output:
+```python
+[{'summary_text': 'An optical coherent receiver for an optical communication network includes a power adjuster and a digital circuit connected between output and input of the multiplying unit and configured to compute a common gain indicative of a sum of the power of an in-phase component and the power-adjusted quadrature component, and the differential gain as a product between the common gain and the diffractive gain.'}]
+```
+
+Alternatively, you can load the model and use it as follows:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelWithLMHead
+# cuda/cpu
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = AutoTokenizer.from_pretrained("turingmachine/hupd-t5-small")
+model = AutoModelWithLMHead.from_pretrained("turingmachine/hupd-t5-small").to(device)
+
+inputs = tokenizer(TEXT, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    outputs = model.generate(inputs.input_ids, max_new_tokens=256)
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+## Citation
+
+For more information, please take a look at the original paper.
+
+* Paper: [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](TBD)
+
+* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* 
+
+* BibTeX:
+```
+@article{suzgun2022hupd,
+  title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
+  author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K and Kominers, Scott and Shieber, Stuart},
+  year={2022}
+}
+```