mGPT 1.3B
Created at 12.01.2024 11:17Assessment of the main tasks: 0.198
The table will scroll to the left
| Task name | Result | Metric |
|---|---|---|
| LCS | 0.136 | Accuracy |
| RCB | 0.333 / 0.167 | Avg. F1 / Accuracy |
| USE | 0 | Grade Norm |
| RWSD | 0.519 | Accuracy |
| PARus | 0.498 | Accuracy |
| ruTiE | 0.5 | Accuracy |
| MultiQ | 0.055 / 0.014 | F1-score/EM |
| CheGeKa | 0.004 / 0 | F1 / EM |
| ruModAr | 0.001 | EM |
| ruMultiAr | 0.012 | EM |
| MathLogicQA | 0.258 | Accuracy |
| ruWorldTree | 0.251 / 0.225 | Avg. F1 / Accuracy |
| ruOpenBookQA | 0.245 / 0.193 | 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 | 0.449 | Accuracy | ||||||||||||||||||||||||
| ruMMLU | 0.241 | Accuracy | ||||||||||||||||||||||||
| SimpleAr | 0.007 | EM | ||||||||||||||||||||||||
| ruHumanEval | 0 / 0 / 0 | pass@k | ||||||||||||||||||||||||
| ruHHH |
0.478
|
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:
[[0.071, 0.083
, 0.092, 0.075
, 0.12], |
5 MCC |
Information about the submission:
Team:
MERA
Name of the ML model:
mGPT 1.3B
Link to the ML model:
https://huggingface.co/ai-forever/mGPTAdditional links:
https://arxiv.org/pdf/2204.07580.pdfArchitecture description:
The mGPT architecture is based on GPT-3. We use the architecture description by Brown et al., the GPT-2 code base (Radford et al., 2019) from HuggingFace (Wolf et al., 2020) and MegatronLM (Shoeybi et al., 2020). With all the other hyperparameters equal, GPT-3 has fewer layers (Layers: 48 vs. 24) but a larger hidden size (dmodel: 1600 vs. 2048) as opposed to GPT-2. GPT-3 also alternates the classic dense and sparse attention layers (Child et al., 2019).
Description of the training:
LM was pretrained with a total batch size of 2048 and a context window of 512 tokens. The total number of the training steps is 600k, and the models have seen 400B tokens during pretraining. The pretraining took 22 days on a cluster of 512 V100 GPUs for mGPT13B.
Pretrain data:
The pretraining corpus represents a collection of documents from Wikipedia and C4. The Wikipedia texts are extracted from the dumps (v. 20201101) with WikiExtractor (Attardi, 2015). The C4 data is downloaded using the Tensorflow datasets(Paper, 2021).
Training Details:
Fixed hyperparameters: vocabulary size of 100k, learning rate of 2e−4, and batch size of 4.
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
