Text Classification
Transformers
PyTorch
TensorFlow
English
roberta
marketing mix
multi-label
classification
microblog
tweets
text-embeddings-inference
Instructions to use dmr76/mmx_classifier_microblog_ENv02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmr76/mmx_classifier_microblog_ENv02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dmr76/mmx_classifier_microblog_ENv02")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dmr76/mmx_classifier_microblog_ENv02") model = AutoModelForSequenceClassification.from_pretrained("dmr76/mmx_classifier_microblog_ENv02", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7690435368237a6bdc1b7eeda89180fa6b8d878f9a7bca8d4c0b0736432a0a9
- Size of remote file:
- 1.42 GB
- SHA256:
- 2ff0fbe89712ad3626b5d4db25ad64d5783b88738ab668fdf6e6d4cdb6f4cf3e
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