Update README.md

README.md

@@ -5,4 +5,55 @@ language:
 base_model:
 - google-bert/bert-base-uncased
 library_name: transformers
----
+---
+
+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 libs)
+
+```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_gts_pretrained.py")
+post =  hf_hub_download(repo_id="gauneg/bert-gts-absa-triple", filename="post.py")
+
+sys.path.append(bert_gts_pretrained)
+sys.path.append(post)
+
+from bert_gts_model import GTSBertBaseABSATriple
+from token_decode_module import DecodeAndEvaluate
+from bert_gts_pretrained import GTSBertBaseABSATriple
+from post import DecodeAndEvaluate
+
+
+from transformers import AutoTokenizer
+
+model_id = 'gauneg/bert-gts-absa-triple'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = GTSBertBaseABSATriple.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))
+
+```
+Expected output
+
+```bash
+[['display', 'well - rounded', 'positive'],
+ ['display', 'great', 'positive'],
+ ['battery life', 'great', 'positive']]
+```