Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use NawinCom/BBC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NawinCom/BBC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NawinCom/BBC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NawinCom/BBC") model = AutoModelForSequenceClassification.from_pretrained("NawinCom/BBC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - text-classification | |
| widget: | |
| - text: "I love AutoTrain" | |
| datasets: | |
| - BBC/autotrain-data | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 0.11368879675865173 | |
| f1_macro: 0.9748328397861948 | |
| f1_micro: 0.9752808988764045 | |
| f1_weighted: 0.9752071164560256 | |
| precision_macro: 0.9752973544608207 | |
| precision_micro: 0.9752808988764045 | |
| precision_weighted: 0.9756012580457148 | |
| recall_macro: 0.9748949579831934 | |
| recall_micro: 0.9752808988764045 | |
| recall_weighted: 0.9752808988764045 | |
| accuracy: 0.9752808988764045 | |