Text Classification
Transformers
PyTorch
JAX
Safetensors
English
bert
news
classification
mini
Eval Results (legacy)
Instructions to use mrm8488/bert-mini-finetuned-age_news-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert-mini-finetuned-age_news-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/bert-mini-finetuned-age_news-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-mini-finetuned-age_news-classification") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/bert-mini-finetuned-age_news-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mrm8488/bert-mini-finetuned-age_news-classification: direct link, hf CLI and curl.
- Browser
- Download file 44.7 MB
-
https://huggingface.co/mrm8488/bert-mini-finetuned-age_news-classification/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mrm8488/bert-mini-finetuned-age_news-classification/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mrm8488/bert-mini-finetuned-age_news-classification/resolve/main/flax_model.msgpack
44.7 MB
- Xet hash:
- 0b7b67d7285d7b567a167a72b48609e3a068584f3d635f1ed8d39b0c680d4394
- Size of remote file:
- 44.7 MB
- SHA256:
- 0d0dd8cf6bd53b2414575bb3658103997689889cff3a01e66341a3b73e49b8a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.