Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use roncc13/trainCMDBERT-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roncc13/trainCMDBERT-sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roncc13/trainCMDBERT-sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("roncc13/trainCMDBERT-sample") model = AutoModelForSequenceClassification.from_pretrained("roncc13/trainCMDBERT-sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from roncc13/trainCMDBERT-sample: direct link, hf CLI and curl.
- Browser
- Download file 361 Bytes
-
https://huggingface.co/roncc13/trainCMDBERT-sample/resolve/main/README.md
- Command line
-
hf download hf://roncc13/trainCMDBERT-sample/README.md
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curl -L -o README.md https://huggingface.co/roncc13/trainCMDBERT-sample/resolve/main/README.md
361 Bytes
metadata
library_name: transformers
tags:
- autotrain
- text-classification
base_model: dost-asti/BERT-ceb-cased
widget:
- text: I love AutoTrain
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.04441085457801819
f1: 0.9523809523809523
precision: 0.9090909090909091
recall: 1.0
auc: 1.0
accuracy: 0.95