Instructions to use blairjdaniel/blairjdaniel_LLM_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blairjdaniel/blairjdaniel_LLM_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="blairjdaniel/blairjdaniel_LLM_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("blairjdaniel/blairjdaniel_LLM_model") model = AutoModelForSequenceClassification.from_pretrained("blairjdaniel/blairjdaniel_LLM_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
blairjdaniel_LLM_model
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6913
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:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1596 | 1.0 | 469 | 0.2283 |
| 0.1874 | 2.0 | 938 | 0.2456 |
| 0.063 | 3.0 | 1407 | 0.3410 |
| 0.0515 | 4.0 | 1876 | 0.4461 |
| 0.0033 | 5.0 | 2345 | 0.5315 |
| 0.0001 | 6.0 | 2814 | 0.6022 |
| 0.0001 | 7.0 | 3283 | 0.6663 |
| 0.0001 | 8.0 | 3752 | 0.6917 |
| 0.0 | 9.0 | 4221 | 0.7192 |
| 0.0 | 10.0 | 4690 | 0.6913 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for blairjdaniel/blairjdaniel_LLM_model
Base model
google-bert/bert-base-uncased