import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import pandas as pd
# 加载ABSA模型和分词器
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# 生成方面和情感
aspects = []
sentiments = []
for index, row in df.iterrows():
    text = row['text']
    row_aspects = row['aspects']
    
    aspect_sentiments = []
    
    for aspect in row_aspects:
        inputs = tokenizer(text, aspect, return_tensors="pt")
        
        with torch.inference_mode():
            outputs = model(**inputs)
        
        predicted_sentiment = torch.argmax(outputs.logits).item()
        sentiment_label = model.config.id2label[predicted_sentiment]
        
        aspect_sentiments.append(f"{aspect}: {sentiment_label}")
    
    aspects.append(row_aspects)
    sentiments.append(aspect_sentiments)
# 将生成的方面和情感添加到DataFrame中
df['generated_aspects'] = aspects
df['generated_sentiments'] = sentiments
# 打印更新后的DataFrame
print(df)

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
aspects = ["food", "service"]
text = "The food was great but the service was terrible."
sentiment_aspect = {}
for aspect in aspects:
    inputs = tokenizer(text, aspect, return_tensors="pt")
    with torch.inference_mode():
        outputs = model(**inputs)
    scores = F.softmax(outputs.logits[0], dim=-1)
    label_id = torch.argmax(scores).item()
    sentiment_aspect[aspect] = (model.config.id2label[label_id], scores[label_id].item())
print(sentiment_aspect)

# Load the ABSA model and tokenizer
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
for aspect in ['camera', 'phone']:
   print(aspect, classifier('The camera quality of this phone is amazing.',  text_pair=aspect))

camera [{'label': 'Positive', 'score': 0.9967294931411743}]
phone [{'label': 'Neutral', 'score': 0.9472787380218506}]

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
# Load the ABSA model and tokenizer
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
pipe = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
pipe("The camera quality of this phone is amazing.", candidate_labels=["camera", "phone"])

{'sequence': 'The camera quality of this phone is amazing.',
 'labels': ['camera', 'phone'],
 'scores': [0.9036691784858704, 0.09633082151412964]}

from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer2 = AutoTokenizer.from_pretrained("yanekyuk/bert-uncased-keyword-extractor")
model2 = AutoModelForTokenClassification.from_pretrained("yanekyuk/bert-uncased-keyword-extractor")
def extract_aspect(text):
    extractor = pipeline("ner", model=model2, tokenizer=tokenizer2)
    phrasesids = []
    for tag in extractor(text):
        if tag['entity'].startswith('B'):
            phrasesids.append([tag['start'], tag['end']])
        if tag['entity'].startswith('I'):
            phrasesids[-1][-1] = tag['end']
    phrases = [text
:p[1]] for p in phrasesids]
    return phrases
text = "The camera quality of this phone is amazing."
extract_aspect(text)

camera

from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
from transformers import pipeline
# Load the ABSA model and tokenizer
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
tokenizer2 = AutoTokenizer.from_pretrained("yanekyuk/bert-uncased-keyword-extractor")
model2 = AutoModelForTokenClassification.from_pretrained("yanekyuk/bert-uncased-keyword-extractor")
def extract_aspect(text):
    extractor = pipeline("ner", model=model2, tokenizer=tokenizer2)
    phrasesids = []
    for tag in extractor(text):
        if tag['entity'].startswith('B'):
            phrasesids.append([tag['start'], tag['end']])
        if tag['entity'].startswith('I'):
            phrasesids[-1][-1] = tag['end']
    phrases = [text
:p[1]] for p in phrasesids]
    return phrases
text = "The camera quality of this phone is amazing."
pipe(text, candidate_labels=extract_aspect(text))

{'sequence': 'The camera quality of this phone is amazing.',
 'labels': ['camera'],
 'scores': [0.9983300566673279]}

<details>
<summary>英文:</summary>
Specific to the `yangheng/deberta-v3-base-absa-v1.1` model this is the usage and you have to loop through the model one time per aspect:

[out]:

----
To get the zero-shot classification scores in general, try using `pipeline`:

[out]:

---- 
Depending on what "text generated aspect" means, perhaps it's keyword extraction, and if so, doing a search on https://huggingface.co/models?search=keyword, gives this as the top downloaded model, https://huggingface.co/yanekyuk/bert-uncased-keyword-extractor

[out]:

Putting the extractor and classifier together:

[out]:

-----
### Q: But the extracted keywords is not "right" or doesn't match the pre-defined ones?
A: No model is perfect and the model example above is a keyword extractor not a product aspect extractor. YMMV.
### Q: Why isn't the zero-shot classifier giving me negative / positive labels? 
A: The zero-shot classifier is labelling the data based on the extracted labels. Not a sentiment classifier. 
</details>