Update README.md

README.md

@@ -1,82 +1,83 @@
----
-license: apache-2.0
----
-
-his work is based on [Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction](https://aclanthology.org/2020.findings-emnlp.234/).The code from 
-their [github repository](https://github.com/NJUNLP/GTS) was also utilized along with their dataset. 
-
-
-This model requires custom code as it uses GridTaggingScheme to predict the labels on the input. For the convenience, 
-the custom code and model architecture has been included with the model.
-
-## Example Code for inferencing
-
-### STEP 1 (Installing huggingface lib)
-
-```bash
-pip install --upgrade huggingface_hub
-```
-
-### STEP 2 (Download the custom code and model to predict opinion target, opinion span and sentiment polarity)
-```python
-
-from huggingface_hub import hf_hub_download
-import sys
-# Download the custom model code
-bert_gts_pretrained = hf_hub_download(repo_id='gauneg/bert-gts-absa-triple', filename="bert_opinion.py")
-post =  hf_hub_download(repo_id='gauneg/bert-gts-absa-triple', filename="post.py")
-
-sys.path.append(bert_gts_pretrained.rsplit("/", 1)[0])
-sys.path.append(post.rsplit("/", 1)[0])
-
-
-from bert_opinion import BertGTSOpinionTriple
-from post import DecodeAndEvaluate
-
-
-from transformers import AutoTokenizer
-
-
-model_id = 'gauneg/bert-gts-absa-triple'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = BertGTSOpinionTriple.from_pretrained(model_id)
-dec_and_infer = DecodeAndEvaluate(tokenizer)
-test_sentence0 = """I charge it at night and skip taking the cord with me because of the good battery life ."""
-test_sentence = "The Dell Inspiron 14 Plus is the most well-rounded laptop with great display and battery life that money can buy."
-
-
-# prediction
-print(dec_and_infer.decode_predict_string_one(test_sentence, model, max_len=128))
-
-```
-Expected output
-
-```bash
-[['display', 'well - rounded', 'positive'],
- ['display', 'great', 'positive'],
- ['battery life', 'great', 'positive']]
-```
-
-# DETAILS
-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 
-in the following diagram:
-
-<figure>
-  <img src="./gts_pic.png" alt="gts-image" style="width:45%">
-  <figcaption>Fig 1. Grid tagging Scheme from <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a> </figcaption>
-</figure>
-
-In the above sentence there are two absa triples. Each triple is expressed in the following order:
-
-[<span style="color:red">Aspect Term/Opinion Target</span>, <span style="color:#7393B3">opinion span</span>, <span style="color:purple">sentiment polarity</span>]
-
-The model and sample code as shown in the snippet with extract opinion triplets as: [
-[<span style="color:red">hot dogs</span>, <span style="color:#7393B3">top notch</span>, <span style="color:purple">positive</span>],
-[<span style="color:red">coffee</span>, <span style="color:#7393B3">avergae</span>, <span style="color:purple">neutral</span>]
-]
-
-Definitions <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a>:
-
-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.
-2. <span style="color:#7393B3">Opinion Term </span>: Opinion Term refers to the term in a sentence used to express attitudes or opinions explicitly.
+---
+license: apache-2.0
+---
+## Dataset Domain {Restaurant + Laptop} Reviews 
+
+## Overview
+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 
+their [github repository](https://github.com/NJUNLP/GTS) was also utilized along with their dataset. 
+
+This model requires custom code as it uses GridTaggingScheme to predict the labels on the input. For the convenience, 
+the custom code and model architecture has been included with the model.
+
+## Example Code for inferencing
+
+### STEP 1 (Installing huggingface lib)
+
+```bash
+pip install --upgrade huggingface_hub
+```
+
+### STEP 2 (Download the custom code and model to predict opinion target, opinion span and sentiment polarity)
+```python
+
+from huggingface_hub import hf_hub_download
+import sys
+# Download the custom model code
+bert_gts_pretrained = hf_hub_download(repo_id='gauneg/bert-gts-absa-triple', filename="bert_opinion.py")
+post =  hf_hub_download(repo_id='gauneg/bert-gts-absa-triple', filename="post.py")
+
+sys.path.append(bert_gts_pretrained.rsplit("/", 1)[0])
+sys.path.append(post.rsplit("/", 1)[0])
+
+
+from bert_opinion import BertGTSOpinionTriple
+from post import DecodeAndEvaluate
+
+
+from transformers import AutoTokenizer
+
+
+model_id = 'gauneg/bert-gts-absa-triple'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = BertGTSOpinionTriple.from_pretrained(model_id)
+dec_and_infer = DecodeAndEvaluate(tokenizer)
+test_sentence0 = """I charge it at night and skip taking the cord with me because of the good battery life ."""
+test_sentence = "The Dell Inspiron 14 Plus is the most well-rounded laptop with great display and battery life that money can buy."
+
+
+# prediction
+print(dec_and_infer.decode_predict_string_one(test_sentence, model, max_len=128))
+
+```
+Expected output
+
+```bash
+[['display', 'well - rounded', 'positive'],
+ ['display', 'great', 'positive'],
+ ['battery life', 'great', 'positive']]
+```
+
+# DETAILS
+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 
+in the following diagram:
+
+<figure>
+  <img src="./gts_pic.png" alt="gts-image" style="width:45%">
+  <figcaption>Fig 1. Grid tagging Scheme from <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a> </figcaption>
+</figure>
+
+In the above sentence there are two absa triples. Each triple is expressed in the following order:
+
+[<span style="color:red">Aspect Term/Opinion Target</span>, <span style="color:#7393B3">opinion span</span>, <span style="color:purple">sentiment polarity</span>]
+
+The model and sample code as shown in the snippet with extract opinion triplets as: [
+[<span style="color:red">hot dogs</span>, <span style="color:#7393B3">top notch</span>, <span style="color:purple">positive</span>],
+[<span style="color:red">coffee</span>, <span style="color:#7393B3">avergae</span>, <span style="color:purple">neutral</span>]
+]
+
+Definitions <a href="https://aclanthology.org/2020.findings-emnlp.234/">(Wu et al., Findings 2020)</a>:
+
+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.
+2. <span style="color:#7393B3">Opinion Term </span>: Opinion Term refers to the term in a sentence used to express attitudes or opinions explicitly.
 3. <span style="color:purple">Sentiment Polarity</span>: This is the sentiment expressed.