Instructions to use DysfunctionalHuman/bert-allfeat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DysfunctionalHuman/bert-allfeat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DysfunctionalHuman/bert-allfeat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DysfunctionalHuman/bert-allfeat") model = AutoModel.from_pretrained("DysfunctionalHuman/bert-allfeat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from DysfunctionalHuman/bert-allfeat: direct link, hf CLI and curl.
- Browser
- Download file 974 Bytes
-
https://huggingface.co/DysfunctionalHuman/bert-allfeat/resolve/main/README.md
- Command line
-
hf download hf://DysfunctionalHuman/bert-allfeat/README.md
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curl -L -o README.md https://huggingface.co/DysfunctionalHuman/bert-allfeat/resolve/main/README.md
974 Bytes
metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: bert-allfeat
results: []
bert-allfeat
This model is a fine-tuned version of bert-base-uncased 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 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1