File size: 3,079 Bytes
ee19d09 16b2f29 ee19d09 16b2f29 ee19d09 16b2f29 ee19d09 16b2f29 ee19d09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: BERT_pretraining_h_100_wo_deepspeed
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.15387755648267093
---
<!-- 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. -->
# BERT_pretraining_h_100_wo_deepspeed
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7778
- Accuracy: 0.1539
## 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: 1e-05
- train_batch_size: 208
- eval_batch_size: 208
- seed: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100000
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 6.8769 | 0.36 | 10000 | 6.7582 | 0.1101 |
| 6.4647 | 0.71 | 20000 | 6.4764 | 0.1314 |
| 6.3679 | 1.07 | 30000 | 6.3218 | 0.1407 |
| 6.252 | 1.42 | 40000 | 6.2139 | 0.1454 |
| 6.2132 | 1.78 | 50000 | 6.1398 | 0.1478 |
| 6.0407 | 2.13 | 60000 | 6.0774 | 0.1502 |
| 6.0694 | 2.49 | 70000 | 6.0303 | 0.1516 |
| 5.9996 | 2.84 | 80000 | 5.9893 | 0.1521 |
| 5.9166 | 3.2 | 90000 | 5.9553 | 0.1526 |
| 5.8915 | 3.55 | 100000 | 5.9261 | 0.1530 |
| 5.8924 | 3.91 | 110000 | 5.8996 | 0.1534 |
| 5.8972 | 4.26 | 120000 | 5.8814 | 0.1533 |
| 5.8454 | 4.62 | 130000 | 5.8626 | 0.1532 |
| 5.8104 | 4.97 | 140000 | 5.8494 | 0.1534 |
| 5.8461 | 5.33 | 150000 | 5.8378 | 0.1534 |
| 5.8476 | 5.68 | 160000 | 5.8246 | 0.1536 |
| 5.7255 | 6.04 | 170000 | 5.8155 | 0.1532 |
| 5.8431 | 6.39 | 180000 | 5.8068 | 0.1537 |
| 5.7526 | 6.75 | 190000 | 5.7981 | 0.1537 |
| 5.7826 | 7.1 | 200000 | 5.7886 | 0.1537 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|