
# 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="HUPD/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("HUPD/hupd-t5-small")
model = AutoModelWithLMHead.from_pretrained("HUPD/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 Duke and Shieber, Stuart M},
  journal={arXiv preprint arXiv:2207.04043},
  year={2022}
}
```
{"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["hupd", "t5", "summarization", "conditional-generation", "patents"], "datasets": ["HUPD/hupd"]}
transformers pytorch t5 text2text-generation hupd summarization conditional-generation patents en dataset:HUPD/hupd license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #hupd #summarization #conditional-generation #patents #en #dataset-HUPD/hupd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# 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. 

For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository.


### How to Use

You can use this model directly with a pipeline for masked language modeling:



Here is the output:


Alternatively, you can load the model and use it as follows:



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

* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* 

* BibTeX:

# 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. 

For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository. ### How to Use

You can use this model directly with a pipeline for masked language modeling:



Here is the output:


Alternatively, you can load the model and use it as follows:



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

* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* 

* BibTeX:
TAGS
#transformers #pytorch #t5 #text2text-generation #hupd #summarization #conditional-generation #patents #en #dataset-HUPD/hupd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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. 

For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository. ### How to Use

You can use this model directly with a pipeline for masked language modeling:



Here is the output:


Alternatively, you can load the model and use it as follows:



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

* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* 

* BibTeX:
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #hupd #summarization #conditional-generation #patents #en #dataset-HUPD/hupd #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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. 

For more information about the Harvard USPTO Patent Dataset, please feel free to visit the project website or the project's GitHub repository.### How to Use

You can use this model directly with a pipeline for masked language modeling:



Here is the output:


Alternatively, you can load the model and use it as follows:



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

* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber* 

* BibTeX: