murali

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README.md

@@ -1,110 +1,79 @@
----
-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:
-```
-@inproceedings{DBLP:conf/cikm/0008ZL23,
-  author       = {Heng Yang and
-                  Chen Zhang and
-                  Ke Li},
-  editor       = {Ingo Frommholz and
-                  Frank Hopfgartner and
-                  Mark Lee and
-                  Michael Oakes and
-                  Mounia Lalmas and
-                  Min Zhang and
-                  Rodrygo L. T. Santos},
-  title        = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment
-                  Analysis},
-  booktitle    = {Proceedings of the 32nd {ACM} International Conference on Information
-                  and Knowledge Management, {CIKM} 2023, Birmingham, United Kingdom,
-                  October 21-25, 2023},
-  pages        = {5117--5122},
-  publisher    = {{ACM}},
-  year         = {2023},
-  url          = {https://doi.org/10.1145/3583780.3614752},
-  doi          = {10.1145/3583780.3614752},
-  timestamp    = {Thu, 23 Nov 2023 13:25:05 +0100},
-  biburl       = {https://dblp.org/rec/conf/cikm/0008ZL23.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}
-}
+
+---
+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}
+}
 ```

model.safetensors

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