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:
- 2023c90b5ba750774a99bb1775d3bace3fd10969c624008e9dc9850503f0d3e6
- Size of remote file:
- 3.9 kB
- SHA256:
- 7124d00849456bd9368778290206174ec84752f8e136b915fe3ae05baec8696c
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