File size: 1,719 Bytes
efe4340 93d9d4d efe4340 7db0fe3 93d9d4d efe4340 |
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 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- jmhessel/newyorker_caption_contest
model-index:
- name: bridgetower-newyorker-gaudi2-8x
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. -->
# bridgetower-newyorker-gaudi2-8x
> Disclaimer: Those are the results I obtained with an older version of Optimum Habana. With v1.7, it is possible to fit batches of 48 samples and to get better throughput as mentioned in this blog post: https://huggingface.co/blog/bridgetower
This model is a fine-tuned version of [BridgeTower/bridgetower-large-itm-mlm-itc](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-itc) on the jmhessel/newyorker_caption_contest matching dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1147
- Memory Allocated (gb): 20.01
- Max Memory Allocated (gb): 83.39
- Total Memory Available (gb): 93.74
## 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: 40
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 320
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1a0+git37b7ddc
- Datasets 2.13.1
- Tokenizers 0.13.3
|