Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pabagcha/roberta_crypto_profiling_task1_2")
model = AutoModelForSequenceClassification.from_pretrained("pabagcha/roberta_crypto_profiling_task1_2")Quick Links
roberta_crypto_profiling_task1_2
This model is a fine-tuned version of cardiffnlp/twitter-roberta-large-2022-154m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9451
- Accuracy: 0.3765
- F1: 0.3577
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pabagcha/roberta_crypto_profiling_task1_2")