Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use runningsnake/bert-base-sequence-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use runningsnake/bert-base-sequence-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="runningsnake/bert-base-sequence-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("runningsnake/bert-base-sequence-classification") model = AutoModelForSequenceClassification.from_pretrained("runningsnake/bert-base-sequence-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from runningsnake/bert-base-sequence-classification: direct link, hf CLI and curl.
- Browser
- Download file 2.17 kB
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https://huggingface.co/runningsnake/bert-base-sequence-classification/resolve/main/README.md
- Command line
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hf download hf://runningsnake/bert-base-sequence-classification/README.md
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curl -L -o README.md https://huggingface.co/runningsnake/bert-base-sequence-classification/resolve/main/README.md
2.17 kB
metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: runningsnake/bert-base-sequence-classification
results: []
runningsnake/bert-base-sequence-classification
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0825
- Train Accuracy: 0.9766
- Validation Loss: 0.5064
- Validation Accuracy: 0.8431
- Epoch: 2
Model description
More information needed
Intended uses & limitations
More information needed
How to use
More information needed
Limitations and bias
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.2559 | 0.9057 | 0.5082 | 0.8211 | 0 |
| 0.1004 | 0.9673 | 0.5064 | 0.8431 | 1 |
| 0.0825 | 0.9766 | 0.5064 | 0.8431 | 2 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
Evaluation results
More information needed