Instructions to use Neesham/first-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neesham/first-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Neesham/first-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Neesham/first-model") model = AutoModelForMaskedLM.from_pretrained("Neesham/first-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Neesham/first-model: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/Neesham/first-model/resolve/main/README.md
- Command line
-
hf download hf://Neesham/first-model/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Neesham/first-model/resolve/main/README.md
1.65 kB
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Neesham/first-model
results: []
Neesham/first-model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 3.1801
- Validation Loss: 2.8738
- Epoch: 0
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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -954, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 3.1801 | 2.8738 | 0 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2