April 9, 2024, 3:44 p.m.
Team: RCC MSU
Model url: https://huggingface.co/msu-rcc-lair/ruadapt_solar_10.7_darulm_unigram_proj_init_twostage_v1
Dataset | Score | Metric |
---|---|---|
LiDiRus | 0.591 | Matthew`s Corr |
RCB | 0.597 / 0.594 | F1/Acc |
PARus | 0.916 | Accuracy |
MuSeRC | 0.946 / 0.837 | F1a/Em |
TERRa | 0.927 | Accuracy |
RUSSE | 0.739 | Accuracy |
RWSD | 0.844 | Accuracy |
DaNetQA | 0.933 | Accuracy |
RuCoS | 0.82 / 0.797 | F1/EM |
Tikhomirov, M.M. and Chernyshev, D.I., 2024. Improving Large Language Model Russian Adaptation with Preliminary Vocabulary Optimization. Lobachevskii Journal of Mathematics (will be soon) ruadapt Solar-10.7 tuned on train sets using https://github.com/IlyaGusev/rulm&
Tested using https://github.com/IlyaGusev/rulm repo Training config: { "trainer": { "evaluation_strategy": "steps", "per_device_train_batch_size": 1, "per_device_eval_batch_size": 1, "gradient_accumulation_steps": 128, "eval_steps": 100, "save_steps": 100, "logging_steps": 5, "learning_rate": 0.00025, "num_train_epochs": 3, "lr_scheduler_type": "cosine", "warmup_steps": 30, "fp16": true, "bf16": false, "torch_compile": false, "optim": "adamw_torch" }, "lora": { "r": 16, "lora_alpha": 16, "lora_dropout": 0.05, "bias": "none", "target_modules": ["q_proj", "v_proj", "k_proj", "o_proj"], "task_type": "CAUSAL_LM" }, "load_in_8bit": false, "only_target_loss": true, "mode": "chat", "templates_path": "internal_prompts/saiga_v2.json", "model_name": "/data/models/gpt/solar/ruadapt_solar_10.7_darulm_unigram_proj_init_part2_v3_alpha_scale_2", "model_type": "causal", "max_tokens_count": 1024 }
Category | Score |
---|---|
LOGIC | 0.5182171466476416 |
KNOWLEDGE | 0.5686318492790999 |
PREDICATE-ARGUMENT STRUCTURE | 0.5990987736402609 |
LEXICAL SEMANTICS | 0.6824189078791257 |
Lexical Semantics - Lexical Entailment | 0.728252704039083 |
---|---|
Lexical Semantics - Morphological Negation | 0.5114621455194985 |
Lexical Semantics - Factivity | 0.4050074169097869 |
Lexical Semantics - Symmetry/Collectivity | 0.9128709291752769 |
Lexical Semantics - Redundancy | 0.45652173913043476 |
Lexical Semantics - Named Entities | 0.7826237921249264 |
Lexical Semantics - Quantifiers | 0.5936523178536028 |
Predicate-Argument Structure Core Args | 0.7242730345466608 |
Predicate-Argument Structure Prepositional Phrases | 0.6588289607878823 |
Predicate-Argument Structure Ellipsis/Implicits | 0.6963106238227914 |
Predicate-Argument Structure Anaphora/Coreference | 0.4879330934705123 |
Predicate-Argument Structure Active/Passive | 0.6562179588897107 |
Predicate-Argument Structure Nominalization | 0.8006407690254357 |
Predicate-Argument Structure Genitives/Partitives | 0.6875 |
Predicate-Argument Structure Datives | 0.629940788348712 |
Predicate-Argument Structure Relative Clauses | 0.5222329678670935 |
Predicate-Argument Structure Coordination Scopes | 0.5248390246530005 |
Predicate-Argument Structure Intersectivity | 0.4496144015129485 |
Predicate-Argument Structure Restrictivity | 0.4963635881027162 |
Logic Negation | 0.30739085537373884 |
Logic Double Negation | 0.6492207662311682 |
Logic Interval/Numbers | 0.433289122413121 |
Logic Conjuction | 0.6333794997024097 |
Logic Disjunction | 0.5277790490704242 |
Logic Conditionals | 0.6507936507936508 |
Logic Universal | 0.6700593942604899 |
Logic Existential | 0.24232015747572203 |
Logic Temporal | 0.6798418006783489 |
Logic Upward Monotone | 0.7894736842105263 |
Logic Downward Monotone | 0.032570825073951766 |
Logic Non-Monotonic | 0.45044261646145084 |
Knowledge Common Sense | 0.5274820157194837 |
Knowledge World Knowledge | 0.6076559095877964 |
Dataset | Speed | RAM |
---|---|---|
LiDiRus | - | - |
RCB | - | - |
PARus | - | - |
MuSeRC | - | - |
TERRa | - | - |
RUSSE | - | - |
RWSD | - | - |
DaNetQA | - | - |
RuCoS | - | - |