Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("TeeA/roberta-classifier-pokemon")
model = AutoModelForSequenceClassification.from_pretrained("TeeA/roberta-classifier-pokemon")Quick Links
roberta-classifier-pokemon
This model is a fine-tuned version of TeeA/roberta-classifier-pokemon on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 11.3963
- eval_accuracy: 0.0619
- eval_runtime: 6.6209
- eval_samples_per_second: 209.942
- eval_steps_per_second: 13.14
- epoch: 22.0
- step: 6710
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=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TeeA/roberta-classifier-pokemon")