Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RogerB/afro-xlmr-large-unkin-sent2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/afro-xlmr-large-unkin-sent2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerB/afro-xlmr-large-unkin-sent2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerB/afro-xlmr-large-unkin-sent2") model = AutoModelForSequenceClassification.from_pretrained("RogerB/afro-xlmr-large-unkin-sent2") - Notebooks
- Google Colab
- Kaggle
afro-xlmr-large-unkin-sent2
This model is a fine-tuned version of Davlan/afro-xlmr-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8649
- F1: 0.6754
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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1000000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.9788 | 1.0 | 1013 | 0.8178 | 0.6535 |
| 0.7932 | 2.0 | 2026 | 0.6577 | 0.7392 |
| 0.6943 | 3.0 | 3039 | 0.5971 | 0.7688 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- Downloads last month
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Model tree for RogerB/afro-xlmr-large-unkin-sent2
Base model
Davlan/afro-xlmr-large