Update README.md
Browse files
README.md
CHANGED
@@ -10,9 +10,8 @@ datasets:
|
|
10 |
- allenai/c4
|
11 |
---
|
12 |
|
13 |
-
[Google's T5-v1.1-base](https://huggingface.co/google/t5-v1_1-base) pre-trained for 24 hours (80k steps / 256 batch size) in [nanoT5](https://github.com/PiotrNawrot/nanoT5) library for efficient pre-training.
|
14 |
|
15 |
For more details about the model refer to the original [paper](https://arxiv.org/pdf/2002.05202.pdf) and original [model weights](https://huggingface.co/google/t5-v1_1-base).
|
16 |
|
17 |
-
This checkpoint was pre-trained on a single GPU for 20 hours.
|
18 |
It can be further fine-tuned on SuperNatural-Instructions dataset to achieve comparable performance of the same model pre-trained on 150x more data through "a combination of model and data parallelism [...] on slices of Cloud TPU Pods", each with 1024 TPUs.
|
|
|
10 |
- allenai/c4
|
11 |
---
|
12 |
|
13 |
+
[Google's T5-v1.1-base](https://huggingface.co/google/t5-v1_1-base) pre-trained for 24 hours (80k steps / 256 batch size) on a single GPU in [nanoT5](https://github.com/PiotrNawrot/nanoT5) library for efficient pre-training.
|
14 |
|
15 |
For more details about the model refer to the original [paper](https://arxiv.org/pdf/2002.05202.pdf) and original [model weights](https://huggingface.co/google/t5-v1_1-base).
|
16 |
|
|
|
17 |
It can be further fine-tuned on SuperNatural-Instructions dataset to achieve comparable performance of the same model pre-trained on 150x more data through "a combination of model and data parallelism [...] on slices of Cloud TPU Pods", each with 1024 TPUs.
|