Instructions to use am-infoweb/QA_REDACTION_NOV1_18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use am-infoweb/QA_REDACTION_NOV1_18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="am-infoweb/QA_REDACTION_NOV1_18")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("am-infoweb/QA_REDACTION_NOV1_18") model = AutoModelForQuestionAnswering.from_pretrained("am-infoweb/QA_REDACTION_NOV1_18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
QA_REDACTION_NOV1_18
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: 0.1243
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 |
|---|---|---|---|
| 1.1513 | 1.0 | 540 | 0.1070 |
| 0.0807 | 2.0 | 1080 | 0.0882 |
| 0.0201 | 3.0 | 1620 | 0.0497 |
| 0.0661 | 4.0 | 2160 | 0.0649 |
| 0.0776 | 5.0 | 2700 | 1.3211 |
| 1.0758 | 6.0 | 3240 | 1.3761 |
| 1.0832 | 7.0 | 3780 | 0.2234 |
| 0.193 | 8.0 | 4320 | 0.1412 |
| 0.1233 | 9.0 | 4860 | 0.1292 |
| 0.0934 | 10.0 | 5400 | 0.1243 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for am-infoweb/QA_REDACTION_NOV1_18
Base model
FacebookAI/xlm-roberta-base