Instructions to use prajwalJumde/QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajwalJumde/QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="prajwalJumde/QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("prajwalJumde/QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base") model = AutoModelForQuestionAnswering.from_pretrained("prajwalJumde/QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,833 Bytes
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license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# QA_SYNTHETIC_DATA_ONLY_18_AUG_distilbert-base
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0026 | 1.0 | 5344 | 0.0020 |
| 0.0002 | 2.0 | 10688 | 0.0005 |
| 0.006 | 3.0 | 16032 | 0.0002 |
| 0.0263 | 4.0 | 21376 | 0.0000 |
| 0.0009 | 5.0 | 26720 | 0.0000 |
| 0.0031 | 6.0 | 32064 | 0.0001 |
| 0.0001 | 7.0 | 37408 | 0.0000 |
| 0.0 | 8.0 | 42752 | 0.0000 |
| 0.0062 | 9.0 | 48096 | 0.0000 |
| 0.0002 | 10.0 | 53440 | 0.0000 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|