# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("am-infoweb/rap_phase2_04dec")
model = AutoModelForQuestionAnswering.from_pretrained("am-infoweb/rap_phase2_04dec")Quick Links
rap_phase2_04dec
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0032
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1678 | 1.0 | 9639 | 0.0739 |
| 0.0645 | 2.0 | 19278 | 0.0774 |
| 0.0601 | 3.0 | 28917 | 0.0403 |
| 0.0314 | 4.0 | 38556 | 0.0162 |
| 0.2123 | 5.0 | 48195 | 0.0150 |
| 0.0211 | 6.0 | 57834 | 0.0039 |
| 0.0111 | 7.0 | 67473 | 0.0097 |
| 0.0064 | 8.0 | 77112 | 0.0025 |
| 0.0021 | 9.0 | 86751 | 0.0018 |
| 0.0016 | 10.0 | 96390 | 0.0032 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for am-infoweb/rap_phase2_04dec
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="am-infoweb/rap_phase2_04dec")