Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use BigTMiami/amazon_domain_to_classification_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/amazon_domain_to_classification_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BigTMiami/amazon_domain_to_classification_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BigTMiami/amazon_domain_to_classification_test") model = AutoModelForSequenceClassification.from_pretrained("BigTMiami/amazon_domain_to_classification_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from BigTMiami/amazon_domain_to_classification_test: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/BigTMiami/amazon_domain_to_classification_test/resolve/main/README.md
- Command line
-
hf download hf://BigTMiami/amazon_domain_to_classification_test/README.md
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curl -L -o README.md https://huggingface.co/BigTMiami/amazon_domain_to_classification_test/resolve/main/README.md
1.48 kB
metadata
license: mit
base_model: BigTMiami/test_model
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: amazon_domain_to_classification_test
results: []
amazon_domain_to_classification_test
This model is a fine-tuned version of BigTMiami/test_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3816
- Accuracy: 0.8538
- F1 Macro: 0.5005
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.3879 | 1.0 | 1563 | 0.3816 | 0.8538 | 0.5005 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2