Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification") model = AutoModelForSequenceClassification.from_pretrained("BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.77 kB
-
https://huggingface.co/BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification/resolve/main/README.md
- Command line
-
hf download hf://BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification/README.md
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curl -L -o README.md https://huggingface.co/BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification/resolve/main/README.md
1.77 kB
metadata
license: mit
base_model: BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dapt_plus_tapt_amazon_helpfulness_classification
results: []
dapt_plus_tapt_amazon_helpfulness_classification
This model is a fine-tuned version of BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3889
- Accuracy: 0.8696
- F1 Macro: 0.6767
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.317 | 1.0 | 1563 | 0.3193 | 0.8668 | 0.6641 |
| 0.2897 | 2.0 | 3126 | 0.3704 | 0.8702 | 0.6222 |
| 0.2137 | 3.0 | 4689 | 0.3889 | 0.8696 | 0.6767 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2