Update README.md

README.md

@@ -7,4 +7,114 @@ library_name: transformers
 tags:
 - LoRA
 - Adapter
----
+---
+
+# Training 
+This model adapter is designed for token classification tasks, enabling it to extract aspect terms and predict the sentiment polarity associated with the extracted aspect terms. 
+The extracted aspect terms will be the span(s) from the input text on which a sentiment is being expressed. 
+It has been created using [PEFT](https://huggingface.co/docs/peft/index) framework for [LoRA:Low-Rank Adaptation](https://arxiv.org/abs/2106.09685).
+
+## Datasets
+This model has been trained on the following datasets:
+
+1. Aspect Based Sentiment Analysis SemEval Shared Tasks ([2014](https://aclanthology.org/S14-2004/), [2015](https://aclanthology.org/S15-2082/), [2016](https://aclanthology.org/S16-1002/))
+2. Multi-Aspect Multi-Sentiment [MAMS](https://aclanthology.org/D19-1654/)
+
+# Use
+
+* Loading the base model and merging it with LoRA parameters
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from peft import PeftModel
+
+# preparing the labels
+labels = {"B-neu": 1, "I-neu": 2, "O": 0, "B-neg": 3, "B-con": 4, "I-pos": 5, "B-pos": 6, "I-con": 7, "I-neg": 8, "X": -100}
+id2labels = {k:lab for lab, k in labels.items()}
+labels2ids = {k:lab for lab, k in id2labels.items()}
+
+# loading tokenizer and base_model
+base_id = 'FacebookAI/roberta-large'
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+base_model = AutoModelForTokenClassification.from_pretrained(base_id, num_labels=len(labels), id2label=id2labels, label2id=labels2ids)
+
+# using this adapter with base model 
+model = PeftModel.from_pretrained(base_model, 'gauneg/roberta-large-absa-ate-sentiment-lora-adapter', is_trainable=False)
+
+```
+
+This model can be utilized in the following two methods:
+1. Making token level inference
+2. Using pipelines for end to end inference
+
+## Making token level inference
+
+```python
+# after loading base model and the adapter as shown in the previous snippet
+
+text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
+tok_inputs = tokenizer(text_input, return_tensors="pt").to(device)
+
+y_pred = model(**tok_inputs) # predicting the logits
+
+y_pred_fin = y_pred.logits.argmax(dim=-1)[0] # selecting the most favoured labels for each token from the logits
+
+decoded_pred = [id2labels[logx.item()] for logx in y_pred_fin]
+
+tok_levl_pred = list(zip(tokenizer.convert_ids_to_tokens(tok_inputs['input_ids'][0]), decoded_pred))[1:-1]
+```
+
+RESULTS in `tok_levl_pred` variable:
+```bash
+[('Be', 'O'),
+ ('en', 'O'),
+ ('Ġhere', 'O'),
+ ('Ġa', 'O'),
+ ('Ġfew', 'O'),
+ ('Ġtimes', 'O'),
+ ('Ġand', 'O'),
+ ('Ġfood', 'B-pos'),
+ ('Ġhas', 'O'),
+ ('Ġalways', 'O'),
+ ('Ġbeen', 'O'),
+ ('Ġgood', 'O'),
+ ('Ġbut', 'O'),
+ ('Ġservice', 'B-neg'),
+ ('Ġreally', 'O'),
+ ('Ġsuffers', 'O'),
+ ('Ġwhen', 'O'),
+ ('Ġit', 'O'),
+ ('Ġgets', 'O'),
+ ('Ġcrowded', 'O'),
+ ('.', 'O')]
+```
+
+## Using end-to-end token classification pipeline
+
+```python
+# after loading base model and the adapter as shown in the previous snippet
+from transformers import pipeline
+
+ate_senti_pipeline = pipeline(task='ner', 
+                  aggregation_strategy='simple',
+                  model=model,
+                  tokenizer=tokenizer)
+
+
+text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
+ate_senti_pipeline(text_input)
+
+```
+OUTPUT
+```bash
+[{'entity_group': 'pos',
+  'score': 0.92310727,
+  'word': ' food',
+  'start': 26,
+  'end': 30},
+ {'entity_group': 'neg',
+  'score': 0.90695626,
+  'word': ' service',
+  'start': 56,
+  'end': 63}]
+```