diff --git a/code_completion/eval/config/config.yaml b/code_completion/eval/config/config.yaml
index 70941e9..9d0a7b6 100644
--- a/code_completion/eval/config/config.yaml
+++ b/code_completion/eval/config/config.yaml
@@ -1,8 +1,8 @@
 defaults:
   - params: codellama7b
   - composers_config: python_standard
+  - dataset: small
 
-dataset: "JetBrains-Research/lca-codegen-small"
 language: 'python'
 
 artifacts_dir: "data/code_generation/artifacts/"
diff --git a/code_completion/eval/config/dataset/huge.yaml b/code_completion/eval/config/dataset/huge.yaml
new file mode 100644
index 0000000..c253ab3
--- /dev/null
+++ b/code_completion/eval/config/dataset/huge.yaml
@@ -0,0 +1,2 @@
+path: "JetBrains-Research/lca-project-level-code-completion"
+name: "huge_context"
diff --git a/code_completion/eval/config/dataset/large.yaml b/code_completion/eval/config/dataset/large.yaml
new file mode 100644
index 0000000..d1f50cc
--- /dev/null
+++ b/code_completion/eval/config/dataset/large.yaml
@@ -0,0 +1,2 @@
+path: "JetBrains-Research/lca-project-level-code-completion"
+name: "large_context"
diff --git a/code_completion/eval/config/dataset/medium.yaml b/code_completion/eval/config/dataset/medium.yaml
new file mode 100644
index 0000000..118a1d5
--- /dev/null
+++ b/code_completion/eval/config/dataset/medium.yaml
@@ -0,0 +1,2 @@
+path: "JetBrains-Research/lca-project-level-code-completion"
+name: "medium_context"
diff --git a/code_completion/eval/config/dataset/small.yaml b/code_completion/eval/config/dataset/small.yaml
new file mode 100644
index 0000000..6e9adad
--- /dev/null
+++ b/code_completion/eval/config/dataset/small.yaml
@@ -0,0 +1,2 @@
+path: "JetBrains-Research/lca-project-level-code-completion"
+name: "small_context"
diff --git a/code_completion/eval/eval_pipeline.py b/code_completion/eval/eval_pipeline.py
index 387be25..bfc448b 100644
--- a/code_completion/eval/eval_pipeline.py
+++ b/code_completion/eval/eval_pipeline.py
@@ -2,6 +2,7 @@
 import warnings
 
 import hydra
+import omegaconf
 import torch.cuda
 from omegaconf import DictConfig
 
@@ -22,7 +23,7 @@
 @dataclass
 class PreprocessConfig:
     model: str  # One of PREPROCESSORS from lca.code_generation.eval.preprocess
-    dataset: str  # Path to dataset
+    dataset: str | omegaconf.dictconfig.DictConfig  # Path to dataset or dictionary with `path`, `name` keys
     tokenizer: str  # Path to tokenizer
     # config_path: str  # Path to composer configs
     composers: str  # One of COMPOSERS from lca.code_generation.eval.preprocess
@@ -75,7 +76,10 @@ def __init__(self, config, composers=COMPOSERS):
             warnings.warn(f'Model and Tokenizer have different paths')
 
         # preprocess_params.dataset = config.dataset
-        self.dataset_name = config.dataset.split('/')[-1].replace('-', '_')
+        if isinstance(config.dataset, str):
+            self.dataset_name = config.dataset.split('/')[-1].replace('-', '_')
+        elif isinstance(config.dataset, omegaconf.dictconfig.DictConfig):
+            self.dataset_name = config.dataset['name']
         dataset_out_dir = os.path.join(config.artifacts_dir, config.language, inference_params['model'],
                                        self.dataset_name)
         # preprocess_params['out_dir'] = os.path.join(dataset_out_dir, 'in')
@@ -152,7 +156,7 @@ def run(self):
             gen_scores, gen_results, em_difference, line_counts = evaluate_generation(self.generator_config)
 
             wb_run.log(gen_scores | {'EM_difference': em_difference, 'Line Counts': line_counts,
-                                     "dataset": self.config.dataset, "model": self.inference_args.model})
+                                     "dataset": self.dataset_name, "model": self.inference_args.model})
             wb_run.finish()
             with open(os.path.join(self.out_dir, 'generation_scores.json'), 'w') as f:
                 json.dump(gen_results, f, indent=4)
@@ -180,7 +184,7 @@ def run_zero_context(self):
         print(">>Evaluation...")
         mean_ppl = evaluate(self.eval_args)
 
-        return {"perplexity": mean_ppl, "context": 0, "composer": "zero", "dataset": self.config.dataset,
+        return {"perplexity": mean_ppl, "context": 0, "composer": "zero", "dataset": self.dataset_name,
                 "model": self.inference_args.model} | lost_tokens
 
     def run_composer(self, composer, results):
@@ -208,7 +212,7 @@ def run_composer(self, composer, results):
             print(">>>>>>Evaluation...")
             mean_ppl = evaluate(self.eval_args)
             results.append({"perplexity": mean_ppl, "context": self.inference_args.context_max,
-                            "composer": composer, "dataset": self.config.dataset,
+                            "composer": composer, "dataset": self.dataset_name,
                             "model": self.inference_args.model} | lost_tokens)
             print(results[-1])
             wb_run.log(results[-1])
diff --git a/code_completion/eval/preprocess.py b/code_completion/eval/preprocess.py
index 6b1066e..e217f27 100644
--- a/code_completion/eval/preprocess.py
+++ b/code_completion/eval/preprocess.py
@@ -52,7 +52,7 @@ def preprocess(args, composer_args):
     prepared_dataset_path = os.path.join(resolve_directories(args), f'model_inputs_composer_{args.composers}.json')
 
     preprocessor = get_preprocessor(args)(
-        filepath=args.dataset,
+        dataset_params=args.dataset,
         tokenizer_path=args.tokenizer,
         context_len_char=args.context_len_char,
         **composers
diff --git a/code_completion/eval/preprocessors.py b/code_completion/eval/preprocessors.py
index 50d801c..f6836cf 100644
--- a/code_completion/eval/preprocessors.py
+++ b/code_completion/eval/preprocessors.py
@@ -3,6 +3,8 @@
 import os.path
 from typing import Dict, List, Any, Optional, Callable
 from dataclasses import dataclass
+
+import omegaconf
 import torch
 from datasets import load_dataset
 from joblib import Parallel, delayed
@@ -21,15 +23,15 @@ class TokenizerOutput:
 
 class PreprocessorBase:
     def __init__(self,
-                 filepath: str,
+                 dataset_params: str | dict,
                  tokenizer_path: str | None = None,
                  context_len_char: int = 60_000,
                  context_composer: Callable[[Dict[str, Any]], str] | None = None,
                  completion_composer: Callable[[Dict[str, Any]], str] | None = None,
                  data_source: str = 'hf',
                  ):
-        self.filepath = filepath
-        self.data: list[DatapointBase] = self._load_data(filepath)
+        self.dataset_params = dataset_params
+        self.data: list[DatapointBase] = self._load_data(dataset_params)
         self.prepared_data: Optional[List[Dict[str, Any]]] = None
         self.tokenizer_path = tokenizer_path
         self.context_composer = context_composer
@@ -124,10 +126,16 @@ def save_model_inputs(self, filepath='lca/code_generation/data/model_inputs.json
         with open(filepath, 'w') as f:
             json.dump(self.prepared_data, f)
 
-    def _load_data(self, path: str) -> list[DatapointBase]:
+    def _load_data(self, dataset_params: str | dict) -> list[DatapointBase]:
         if True: #self.data_source == 'hf':
             data = list()
-            hf_data = load_dataset(path, split='test')
+            if isinstance(dataset_params, str):
+                hf_data = load_dataset(dataset_params, split='test')
+            elif isinstance(dataset_params, omegaconf.dictconfig.DictConfig):
+                hf_data = load_dataset(split='test', **dataset_params)
+            else:
+                raise ValueError('check `config.dataset`, it must be string or dictionary')
+
             repos_list = list(set([hf_dp['repo'] for hf_dp in hf_data]))
             repos_map = {repo: repo_num for repo_num, repo in enumerate(repos_list)}
 
@@ -153,8 +161,8 @@ def _load_data(self, path: str) -> list[DatapointBase]:
 
 import youtokentome as yttm
 class FLPythonPreprocessor(PreprocessorBase):
-    def __init__(self, filepath, tokenizer_path=None, context_len_char=60_000, **composers):
-        super().__init__(filepath, tokenizer_path, context_len_char, **composers)
+    def __init__(self, dataset_params, tokenizer_path=None, context_len_char=60_000, **composers):
+        super().__init__(dataset_params, tokenizer_path, context_len_char, **composers)
         self.lang_sep_symbol = '₣'
         self.meta_info_sep_symbol = '𐌼'
         self.extension = '.py'
@@ -192,8 +200,8 @@ def _load_tokenizer(self, path):
 
 from transformers import AutoTokenizer
 class HFPreprocessor(PreprocessorBase):
-    def __init__(self, filepath, tokenizer_path, context_len_char=60_000, **composers):
-        super().__init__(filepath, tokenizer_path, context_len_char, **composers)
+    def __init__(self, dataset_params, tokenizer_path, context_len_char=60_000, **composers):
+        super().__init__(dataset_params, tokenizer_path, context_len_char, **composers)
         self.lang_sep_symbol = ''
         self.meta_info_sep_symbol = 'METASEP'
         self.extension = ''
@@ -222,8 +230,8 @@ def _load_tokenizer(self, path):
 
 
 class StarcoderPreprocessor(HFPreprocessor):
-    def __init__(self, filepath, tokenizer_path="bigcode/starcoder", context_len_char=60_000, **composers):
-        super().__init__(filepath, tokenizer_path, context_len_char, **composers)
+    def __init__(self, dataset_params, tokenizer_path="bigcode/starcoder", context_len_char=60_000, **composers):
+        super().__init__(dataset_params, tokenizer_path, context_len_char, **composers)
         self.lang_sep_symbol = 'LANGSEP'
         self.meta_info_sep_symbol = 'METASEP'
         self.extension = '.py'