Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# 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")Quick Links
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
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Model tree for BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BigTMiami/dapt_plus_tapt_amazon_helpfulness_classification")