yangheng
/

deberta-v3-base-absa-v1.1

@@ -1,79 +1,101 @@
----
-language:
-- en
-tags:
-- aspect-based-sentiment-analysis
-- PyABSA
-license: mit
-datasets:
-- laptop14
-- restaurant14
-- restaurant16
-- ACL-Twitter
-- MAMS
-- Television
-- TShirt
-- Yelp
-metrics:
-- accuracy
-- macro-f1
-widget:
-- text: "[CLS] when tables opened up, the manager sat another party before us. [SEP] manager [SEP] "
----
-# Note
-This model is training with 30k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
-# DeBERTa for aspect-based sentiment analysis
-The `deberta-v3-base-absa` model for aspect-based sentiment analysis, trained with English datasets from [ABSADatasets](https://github.com/yangheng95/ABSADatasets).
-## Training Model
-This model is trained based on the FAST-LCF-BERT model with `microsoft/deberta-v3-base`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA).
-To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA).
-## Usage
-```python3
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
-model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
-```
-## Example in PyASBA
-An [example](https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/train_apc_multilingual.py) for using FAST-LCF-BERT in PyASBA datasets.
-## Datasets
-This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
-```
-loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg
-loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg
-loading: integrated_datasets/apc_datasets/SemEval/restaurant16/restaurant_train.raw
-loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/train.raw
-loading: integrated_datasets/apc_datasets/MAMS/train.xml.dat
-loading: integrated_datasets/apc_datasets/Television/Television_Train.xml.seg
-loading: integrated_datasets/apc_datasets/TShirt/Menstshirt_Train.xml.seg
-loading: integrated_datasets/apc_datasets/Yelp/yelp.train.txt
-```
-If you use  this model in your research, please cite our paper:
-```
-@article{YangZMT21,
-  author    = {Heng Yang and
-               Biqing Zeng and
-               Mayi Xu and
-               Tianxing Wang},
-  title     = {Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable
-               Sentiment Dependency Learning},
-  journal   = {CoRR},
-  volume    = {abs/2110.08604},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2110.08604},
-  eprinttype = {arXiv},
-  eprint    = {2110.08604},
-  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08604.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
 ```

+---
+language:
+- en
+tags:
+- aspect-based-sentiment-analysis
+- PyABSA
+license: mit
+datasets:
+- laptop14
+- restaurant14
+- restaurant16
+- ACL-Twitter
+- MAMS
+- Television
+- TShirt
+- Yelp
+metrics:
+- accuracy
+- macro-f1
+widget:
+- text: "[CLS] when tables opened up, the manager sat another party before us. [SEP] manager [SEP] "
+---
+# Powered by [PyABSA](https://github.com/yangheng95/PyABSA): An open source tool for aspect-based sentiment analysis
+This model is training with 30k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
+## Usage
+```python3
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# Load the ABSA model and tokenizer
+model_name = "yangheng/deberta-v3-base-absa-v1.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+for aspect in ['camera', 'phone']:
+   print(aspect, classifier('The camera quality of this phone is amazing.',  text_pair=aspect))
+```
+# DeBERTa for aspect-based sentiment analysis
+The `deberta-v3-base-absa` model for aspect-based sentiment analysis, trained with English datasets from [ABSADatasets](https://github.com/yangheng95/ABSADatasets).
+## Training Model
+This model is trained based on the FAST-LCF-BERT model with `microsoft/deberta-v3-base`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA).
+To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA).
+## Example in PyASBA
+An [example](https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/train_apc_multilingual.py) for using FAST-LCF-BERT in PyASBA datasets.
+## Datasets
+This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
+```
+loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg
+loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg
+loading: integrated_datasets/apc_datasets/SemEval/restaurant16/restaurant_train.raw
+loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/train.raw
+loading: integrated_datasets/apc_datasets/MAMS/train.xml.dat
+loading: integrated_datasets/apc_datasets/Television/Television_Train.xml.seg
+loading: integrated_datasets/apc_datasets/TShirt/Menstshirt_Train.xml.seg
+loading: integrated_datasets/apc_datasets/Yelp/yelp.train.txt
+```
+If you use  this model in your research, please cite our papers:
+@article{YangL22,
+  author    = {Heng Yang and
+               Ke Li},
+  title     = {A Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
+  journal   = {CoRR},
+  volume    = {abs/2208.01368},
+  year      = {2022},
+  url       = {https://doi.org/10.48550/arXiv.2208.01368},
+  doi       = {10.48550/arXiv.2208.01368},
+  eprinttype = {arXiv},
+  eprint    = {2208.01368},
+  timestamp = {Tue, 08 Nov 2022 21:46:32 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2208-01368.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+@article{YangZMT21,
+  author    = {Heng Yang and
+               Biqing Zeng and
+               Mayi Xu and
+               Tianxing Wang},
+  title     = {Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable
+               Sentiment Dependency Learning},
+  journal   = {CoRR},
+  volume    = {abs/2110.08604},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2110.08604},
+  eprinttype = {arXiv},
+  eprint    = {2110.08604},
+  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08604.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
 ```