Instructions to use hf-internal-testing/tiny-bert-h5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-bert-h5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-bert-h5")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert-h5") model = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-h5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,040 Bytes
df5e9ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ---
base_model: hf-internal-testing/tiny-bert-pt-safetensors
tags:
- generated_from_keras_callback
model-index:
- name: tiny-bert-h5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# tiny-bert-h5
This model is a fine-tuned version of [hf-internal-testing/tiny-bert-pt-safetensors](https://huggingface.co/hf-internal-testing/tiny-bert-pt-safetensors) 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 4.36.0.dev0
- TensorFlow 2.13.1
- Datasets 2.14.5
- Tokenizers 0.14.1
|