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---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- hupd
- roberta
- distilroberta
- patents
license: cc-by-sa-4.0
datasets:
- HUPD/hupd
---
# HUPD DistilRoBERTa-Base Model
This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. 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
model = pipeline(task="fill-mask", model="hupd/hupd-distilroberta-base")
model("Improved <mask> for playing a game of thumb wrestling.")
```
Here is the output:
```python
[{'score': 0.4274042248725891,
'sequence': 'Improved method for playing a game of thumb wrestling.',
'token': 5448,
'token_str': ' method'},
{'score': 0.06967400759458542,
'sequence': 'Improved system for playing a game of thumb wrestling.',
'token': 467,
'token_str': ' system'},
{'score': 0.06849079579114914,
'sequence': 'Improved device for playing a game of thumb wrestling.',
'token': 2187,
'token_str': ' device'},
{'score': 0.04544765502214432,
'sequence': 'Improved apparatus for playing a game of thumb wrestling.',
'token': 26529,
'token_str': ' apparatus'},
{'score': 0.025765646249055862,
'sequence': 'Improved means for playing a game of thumb wrestling.',
'token': 839,
'token_str': ' means'}]
```
Alternatively, you can load the model and use it as follows:
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
# cuda/cpu
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained("hupd/hupd-distilroberta-base")
model = AutoModelForMaskedLM.from_pretrained("hupd/hupd-distilroberta-base").to(device)
TEXT = "Improved <mask> for playing a game of thumb wrestling."
inputs = tokenizer(TEXT, return_tensors="pt").to(device)
with torch.no_grad():
logits = model(**inputs).logits
# retrieve indices of <mask>
mask_token_indxs = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
for mask_idx in mask_token_indxs:
predicted_token_id = logits[0, mask_idx].argmax(axis=-1)
output = tokenizer.decode(predicted_token_id)
print(f'Prediction for the <mask> token at index {mask_idx}: "{output}"')
```
Here is the output:
```python
Prediction for the <mask> token at index 2: " method"
```
## 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}
}
``` |