Text Classification
Transformers
PyTorch
English
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use dadashzadeh/cryptocurrency-intent-search-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dadashzadeh/cryptocurrency-intent-search-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dadashzadeh/cryptocurrency-intent-search-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dadashzadeh/cryptocurrency-intent-search-detection") model = AutoModelForSequenceClassification.from_pretrained("dadashzadeh/cryptocurrency-intent-search-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: HooshvareLab/roberta-fa-zwnj-base
tags:
- generated_from_trainer
model-index:
- name: test-trainer
results: []
test-trainer
This model is a fine-tuned version of HooshvareLab/roberta-fa-zwnj-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1195
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.561 | 1.0 | 4473 | 0.3579 |
| 0.3881 | 2.0 | 8946 | 0.2016 |
| 0.1966 | 3.0 | 13419 | 0.1195 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1