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license: apache-2.0 |
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## Dataset Domain: Restaurant Reviews |
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## Overview |
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This work is based on [Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction](https://aclanthology.org/2020.findings-emnlp.234/).The code from |
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their [github repository](https://github.com/NJUNLP/GTS) was also utilized along with their dataset. |
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This model requires custom code as it uses GridTaggingScheme to predict the labels on the input. For the convenience, |
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the custom code and model architecture has been included with the model. |
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## Example Code for inferencing |
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### STEP 1 (Installing huggingface lib) |
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```bash |
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pip install --upgrade huggingface_hub |
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``` |
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### STEP 2 (Download the custom code and model to predict opinion target, opinion span and sentiment polarity) |
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```python |
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from huggingface_hub import hf_hub_download |
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import sys |
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# Download the custom model code |
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bert_gts_pretrained = hf_hub_download(repo_id='gauneg/bert-gts-absa-triple-restaurant', filename="bert_opinion.py") |
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post = hf_hub_download(repo_id='gauneg/bert-gts-absa-triple-restaurant', filename="post.py") |
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sys.path.append(bert_gts_pretrained.rsplit("/", 1)[0]) |
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sys.path.append(post.rsplit("/", 1)[0]) |
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from bert_opinion import BertGTSOpinionTriple |
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from post import DecodeAndEvaluate |
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from transformers import AutoTokenizer |
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model_id = 'gauneg/bert-gts-absa-triple-restaurant' |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = BertGTSOpinionTriple.from_pretrained(model_id) |
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dec_and_infer = DecodeAndEvaluate(tokenizer) |
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test_sentence0 = """The food was good but the ambience was bad""" |
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# prediction |
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print(dec_and_infer.decode_predict_string_one(test_sentence, model, max_len=128)) |
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``` |
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Expected output |
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```bash |
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[['food', 'good', 'positive'], ['ambience', 'bad', 'negative']] |
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``` |
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# DETAILS |
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The model has been trained to use Grid Tagging Scheme (GTS) to predict `Opinion Target`, `Opinion Span` and `Sentiment Polarity`. For the purpose of training this model the domain specific datasets (laptop and restaurant reviews) were combined. The grid tagging example is shown |
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in the following diagram: |
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<figure> |
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<img src="./gts_pic.png" alt="gts-image" style="width:45%"> |
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<figcaption>Fig 1. Grid tagging Scheme from <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a> </figcaption> |
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</figure> |
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In the above sentence there are two absa triples. Each triple is expressed in the following order: |
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[<span style="color:red">Aspect Term/Opinion Target</span>, <span style="color:#7393B3">opinion span</span>, <span style="color:purple">sentiment polarity</span>] |
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The model and sample code as shown in the snippet with extract opinion triplets as: [ |
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[<span style="color:red">hot dogs</span>, <span style="color:#7393B3">top notch</span>, <span style="color:purple">positive</span>], |
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[<span style="color:red">coffee</span>, <span style="color:#7393B3">avergae</span>, <span style="color:purple">neutral</span>] |
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] |
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Definitions <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a>: |
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1. <span style="color:red">Aspect Term/Opinion Target</span>: Aspect term, also known as opinion target, is the word or phrase in a sentence representing feature or entity of products or services. |
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2. <span style="color:#7393B3">Opinion Term </span>: Opinion Term refers to the term in a sentence used to express attitudes or opinions explicitly. |
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3. <span style="color:purple">Sentiment Polarity</span>: This is the sentiment expressed. |
