Model Trained Using AutoTrain

Problem type: Multi-class Classification (3-class Sentiment Classification)

Validation Metrics

If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score.

Loss: 0.4992932379245758
Accuracy: 0.799017824663514
Macro F1: 0.8021508522962549
Micro F1: 0.799017824663514
Weighted F1: 0.7993775463659935
Macro Precision: 0.80406197665167
Micro Precision: 0.799017824663514
Weighted Precision: 0.8000374433849405
Macro Recall: 0.8005261994732908
Micro Recall: 0.799017824663514
Weighted Recall: 0.799017824663514

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Souvikcmsa/autotrain-sentiment_analysis-762923428

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)

OR

from transformers import pipeline

classifier = pipeline("text-classification", model = "Souvikcmsa/BERT_sentiment_analysis")
classifier("I loved Star Wars so much!")# Positive
classifier("A soccer game with multiple males playing. Some men are playing a sport.")# Neutral

modelee / BERT_sentiment_analysis

Model Trained Using AutoTrain

Validation Metrics

Usage

简介

发行版

贡献者

近期动态

modelee / BERT_sentiment_analysis .gitee-modal { width: 500px !important; }

Model Trained Using AutoTrain

Validation Metrics

Usage

简介

发行版

开源评估指数源自 OSS-Compass 评估体系，评估体系围绕以下三个维度对项目展开评估：

贡献者

近期动态

搜索帮助

modelee / BERT_sentiment_analysis