Model trained on Hindi and English data.
Try it out: https://colab.research.google.com/drive/1A_hbsq1vrCeAh3dEMvtwxxNxcNZ1BUyW?usp=sharing
For sample responose on different prompts checkout: https://github.com/manishiitg/hi-llm-eval
Language Hi
Model | implicit_hate | flores | indicwikibio | hellaswag-indic | truthfulqa-hi | boolq-hi | indicheadline | indic-arc-easy | indicqa | indic-arc-challenge | indicsentiment | xlsum-hi | indicxparaphrase | mmlu_hi |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
open-aditi-hi-v2 | 11.5021 | 43.6822 | 0.4846 | 0.2404 | 0.6934 | 0.8541 | 0.4565 | 0.4979 | 0.0795 | 0.4462 | 0.9729 | 0.4213 | 0.6838 | 0.3253 |
OpenHermes-2.5-Mistral-7B | 0.2068 | 30.3465 | 0.3332 | 0.2485 | 0.3234 | 0.5979 | 0.1996 | 0.3523 | 0.2721 | 0.3396 | 0.9048 | 0.1774 | 0.8766 | 0.2769 |
open-aditi-hi-v1 | 8.6105 | 40.2376 | 0.4104 | 0.0848 | 0.4230 | 0.3758 | 0.4248 | 0.3889 | 0.1306 | 0.3558 | 0.8798 | 0.4212 | 0.5939 | 0.1398 |
Airavata | 0.0663 | 58.0555 | 0.0637 | 0.0254 | 0.2122 | 0.0373 | 0.4346 | 0.1128 | 0.1008 | 0.0836 | 0.8437 | 0.4650 | 0.3277 | 0.1336 |
Language En
Model | boolq | hellaswag | mmlu | truthfulqa | xlsum | arc-easy-exact | arc-challenge |
---|---|---|---|---|---|---|---|
OpenHermes-2.5-Mistral-7B | 0.4061 | 0.7999 | 0.5991 | 0.2081 | 0.4328 | 0.8687 | 0.7790 |
open-aditi-hi-v2 | 0.3982 | 0.4738 | 0.5544 | 0.2999 | 0.4349 | 0.8388 | 0.7235 |
open-aditi-hi-v1 | 0.0434 | 0.3509 | 0.2597 | 0.3317 | 0.4288 | 0.7588 | 0.6271 |
Airavata | 0.0437 | 0.0277 | 0.1165 | 0.3586 | 0.4393 | 0.2534 | 0.1630 |
Task: flores Metric: chrf
Task: implicit_hate Metric: chrf
Task: indicsentiment Metric: accuracy
Task: indicxparaphrase Metric: accuracy
Task: boolq-hi Metric: accuracy
Task: truthfulqa-hi Metric: accuracy
Task: indic-arc-easy Metric: accuracy
Task: indicwikibio Metric: bleurt
Task: xlsum-hi Metric: bleurt
Task: indicheadline Metric: bleurt
Task: indic-arc-challenge Metric: accuracy
Task: mmlu_hi Metric: average_acc
Task: indicqa Metric: accuracy
Task: hellaswag-indic Metric: accuracy
Task: arc-easy-exact Metric: accuracy
Task: hellaswag Metric: accuracy
Task: arc-challenge Metric: accuracy
Task: mmlu Metric: average_acc
Task: xlsum Metric: bleurt
Task: boolq Metric: accuracy
Task: truthfulqa Metric: accuracy
Model evaluation on OpenLLM LeaderBoard
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 59.31 |
AI2 Reasoning Challenge (25-Shot) | 59.39 |
HellaSwag (10-Shot) | 82.01 |
MMLU (5-Shot) | 61.41 |
TruthfulQA (0-shot) | 45.84 |
Winogrande (5-shot) | 77.19 |
GSM8k (5-shot) | 30.02 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard59.390
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard82.010
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard61.410
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard45.840
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.190
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard30.020