ruGPT-3.5 13B
Created at 12.01.2024 11:18Assessment of the main tasks: 0.208
The table will scroll to the left
| Task name | Result | Metric |
|---|---|---|
| LCS | 0.132 | Accuracy |
| RCB | 0.331 / 0.194 | Avg. F1 / Accuracy |
| USE | 0.025 | Grade Norm |
| RWSD | 0.523 | Accuracy |
| PARus | 0.504 | Accuracy |
| ruTiE | 0.488 | Accuracy |
| MultiQ | 0.115 / 0.036 | F1-score/EM |
| CheGeKa | 0.037 / 0 | F1 / EM |
| ruModAr | 0.001 | EM |
| ruMultiAr | 0.025 | EM |
| MathLogicQA | 0.258 | Accuracy |
| ruWorldTree | 0.246 / 0.22 | Avg. F1 / Accuracy |
| ruOpenBookQA | 0.223 / 0.208 | Avg. F1 / Accuracy |
Evaluation on open tasks:
It is not taken into account in the overall ratingThe table will scroll to the left
| Task name | Result | Metric | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BPS | 0.492 | Accuracy | ||||||||||||||||||||||||
| ruMMLU | 0.246 | Accuracy | ||||||||||||||||||||||||
| SimpleAr | 0.029 | EM | ||||||||||||||||||||||||
| ruHumanEval | 0.001 / 0.003 / 0.006 | pass@k | ||||||||||||||||||||||||
| ruHHH |
0.472
|
Accuracy | ||||||||||||||||||||||||
| ruHateSpeech |
0.543
|
Accuracy | ||||||||||||||||||||||||
| ruDetox |
|
Overall average score (J) Assessment of the preservation of meaning (SIM) Assessment of naturalness (FL) Style Transfer Accuracy (STA) |
||||||||||||||||||||||||
| ruEthics |
Table results:
[[-0.036, -0.023
, -0.025, -0.017
, -0.016], |
5 MCC |
Information about the submission:
Team:
MERA
Name of the ML model:
ruGPT-3.5 13B
Link to the ML model:
https://huggingface.co/ai-forever/ruGPT-3.5-13BAdditional links:
https://habr.com/ru/companies/sberbank/articles/746736/Architecture description:
ruGPT-3 is a Russian counterpart of GPT-3 (Brown et al., 2020). Model has 13B parameters. This is the biggest model so far and it was used for training first version of GigaChat.
Description of the training:
Model was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above).
Pretrain data:
Model was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documents. Training data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded.
Training Details:
After the final training perplexity for this model was around 8.8 for Russian.
License:
MIT
Strategy, generation and parameters:
Code version v.1.1.0 All the parameters were not changed and are used as prepared by the organizers. Details: - 1 x NVIDIA A100 - dtype auto - Pytorch 2.1.2 + CUDA 12.1 - Transformers 4.36.2 - Context length 2048
