Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RogerB/afro-xlmr-large-kin-sent3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/afro-xlmr-large-kin-sent3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RogerB/afro-xlmr-large-kin-sent3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RogerB/afro-xlmr-large-kin-sent3") model = AutoModelForSequenceClassification.from_pretrained("RogerB/afro-xlmr-large-kin-sent3") - Notebooks
- Google Colab
- Kaggle
afro-xlmr-large-kin-sent3
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: 0.8477
- F1: 0.6696
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: 10000000
- 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 |
|---|---|---|---|---|
| 0.9771 | 1.0 | 1013 | 0.7473 | 0.6913 |
| 0.7837 | 2.0 | 2026 | 0.6177 | 0.7548 |
| 0.7053 | 3.0 | 3039 | 0.6041 | 0.7678 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for RogerB/afro-xlmr-large-kin-sent3
Base model
Davlan/afro-xlmr-large