# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("prajwalJumde/QA_REDACTION_NOV1_19-a1")
model = AutoModelForQuestionAnswering.from_pretrained("prajwalJumde/QA_REDACTION_NOV1_19-a1", device_map="auto")Quick Links
QA_REDACTION_NOV1_19-a1
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: 0.0272
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 |
|---|---|---|---|
| 0.5321 | 1.0 | 1050 | 0.0480 |
| 0.0442 | 2.0 | 2100 | 0.0527 |
| 0.0832 | 3.0 | 3150 | 0.0779 |
| 0.0262 | 4.0 | 4200 | 0.0505 |
| 0.0271 | 5.0 | 5250 | 0.0363 |
| 0.0219 | 6.0 | 6300 | 0.0238 |
| 0.2035 | 7.0 | 7350 | 0.0263 |
| 0.0122 | 8.0 | 8400 | 0.0211 |
| 0.0288 | 9.0 | 9450 | 0.0293 |
| 0.0294 | 10.0 | 10500 | 0.0272 |
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 prajwalJumde/QA_REDACTION_NOV1_19-a1
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="prajwalJumde/QA_REDACTION_NOV1_19-a1")