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README.md

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+---
+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
+Please use (yangheng/deberta-v3-base-absa-v1.1)[https://huggingface.co/yangheng/deberta-v3-base-absa-v1.1], which is smaller and has better performance.
+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-large-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-large`, 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-large-absa-v1.1")
+
+model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-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}
+}
+```

added_tokens.json

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config.json

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+{
+  "_name_or_path": "yangheng/deberta-v3-large-absa",
+  "_num_labels": 3,
+  "architectures": [
+    "DebertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "Negative",
+    "1": "Neutral",
+    "2": "Positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "Negative": 0,
+    "Neutral": 1,
+    "Positive": 2
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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pytorch_metadata.json

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+{
+  "name": "tiwariratnesh/security-classifier",
+  "commit_hash": "319caf575b8586cb97917ac124b231c7831b3c15",
+  "publish_time": "2024-05-01T15:58:37.942124+00:00"
+}

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