Instructions to use prajwalJumde/rap_phase2_MODEL2_14march_10i with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajwalJumde/rap_phase2_MODEL2_14march_10i with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="prajwalJumde/rap_phase2_MODEL2_14march_10i")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("prajwalJumde/rap_phase2_MODEL2_14march_10i") model = AutoModelForQuestionAnswering.from_pretrained("prajwalJumde/rap_phase2_MODEL2_14march_10i", device_map="auto") - Notebooks
- Google Colab
- Kaggle
rap_phase2_MODEL2_14march_10i
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: 0.8618
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: 6
- eval_batch_size: 6
- 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 |
|---|---|---|---|
| 1.0288 | 1.0 | 635 | 0.4846 |
| 0.1988 | 2.0 | 1270 | 0.0774 |
| 0.0674 | 3.0 | 1905 | 0.0092 |
| 0.0313 | 4.0 | 2540 | 0.0116 |
| 0.0208 | 5.0 | 3175 | 0.0008 |
| 0.0151 | 6.0 | 3810 | 0.0095 |
| 0.1051 | 7.0 | 4445 | 0.2179 |
| 0.1422 | 8.0 | 5080 | 0.5416 |
| 0.383 | 9.0 | 5715 | 1.7699 |
| 1.5649 | 10.0 | 6350 | 0.8618 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for prajwalJumde/rap_phase2_MODEL2_14march_10i
Base model
FacebookAI/xlm-roberta-base