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 | - | - |