Text Classification
Transformers
TensorFlow
TensorBoard
Safetensors
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use PDAP/url-relevance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PDAP/url-relevance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PDAP/url-relevance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDAP/url-relevance") model = AutoModelForSequenceClassification.from_pretrained("PDAP/url-relevance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from PDAP/url-relevance: direct link, hf CLI and curl.
- Browser
- Download file 862 Bytes
-
https://huggingface.co/PDAP/url-relevance/resolve/main/README.md
- Command line
-
hf download hf://PDAP/url-relevance/README.md
-
curl -L -o README.md https://huggingface.co/PDAP/url-relevance/resolve/main/README.md
862 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: url-relevance
results: []
url-relevance
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2