Text Classification
Transformers
Safetensors
English
bert
nlp
routing
vision-task-classifier
text-embeddings-inference
Instructions to use beingamanforever/ICM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beingamanforever/ICM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beingamanforever/ICM")# Load model directly from transformers import AutoTokenizer, TaskClassifier tokenizer = AutoTokenizer.from_pretrained("beingamanforever/ICM") model = TaskClassifier.from_pretrained("beingamanforever/ICM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# Task Classification Model (ICM)
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## Model Description
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---
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language: en
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license: mit
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tags:
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- text-classification
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- nlp
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- transformers
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- bert
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- routing
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- vision-task-classifier
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model_name: ICM
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base_model: bert-base-uncased
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pipeline_tag: text-classification
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datasets:
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- synthetic
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tasks:
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- text-classification
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library_name: transformers
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---
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# Task Classification Model (ICM)
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## Model Description
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