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autotrain-BERTBase-TweetEval-1281248998
sasha/autotrain-BERTBase-TweetEval-1281248998
autotrain-BERTBase-TweetEval-1281248998 is a text classification model from sasha. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 1281248998 - CO2 Emissions (in grams): 0.1031
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Aug 19, 2022
- Updated
- Aug 19, 2022
- SHA
- 72b3b3ec
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1281248998
- CO2 Emissions (in grams): 0.1031
Validation Metrics
- Loss: 0.602
- Accuracy: 0.746
- Macro F1: 0.718
- Micro F1: 0.746
- Weighted F1: 0.743
- Macro Precision: 0.740
- Micro Precision: 0.746
- Weighted Precision: 0.744
- Macro Recall: 0.705
- Micro Recall: 0.746
- Weighted Recall: 0.746
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/sasha/autotrain-BERTBase-TweetEval-1281248998
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("sasha/autotrain-BERTBase-TweetEval-1281248998", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- unk
- Created
- Aug 19, 2022
- Updated
- Aug 19, 2022