Instructions to use CouchCat/ma_mlc_v7_distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CouchCat/ma_mlc_v7_distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CouchCat/ma_mlc_v7_distil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil") - Notebooks
- Google Colab
- Kaggle
Description
A Multi-label text classification model trained on a customer feedback data using DistilBert. Possible labels are:
- Delivery (delivery status, time of arrival, etc.)
- Return (return confirmation, return label requests, etc.)
- Product (quality, complaint, etc.)
- Monetary (pending transactions, refund, etc.)
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil")
model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil")
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