Loading deepchem/feat/tests/test_smiles_tokenizer.py +15 −8 Original line number Diff line number Diff line Loading @@ -8,15 +8,22 @@ from transformers import RobertaForMaskedLM class TestSmilesTokenizer(TestCase): """Tests the SmilesTokenizer to load the USPTO vocab file and a ChemBERTa Masked LM model with pre-trained weights..""" def test_tokenize(self): current_dir = os.path.dirname(os.path.realpath(__file__)) vocab_path = os.path.join(current_dir, 'data', 'vocab.txt') tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13] tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13] model = RobertaForMaskedLM.from_pretrained('seyonec/SMILES_tokenized_PubChem_shard00_50k') model = RobertaForMaskedLM.from_pretrained( 'seyonec/SMILES_tokenized_PubChem_shard00_50k') model.num_parameters() tokenizer = SmilesTokenizer(vocab_path, max_len=model.config.max_position_embeddings) tokenizer = SmilesTokenizer( vocab_path, max_len=model.config.max_position_embeddings) assert tokenized_smiles == tokenizer.encode("CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1") assert tokenized_smiles == tokenizer.encode( "CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1") Loading
deepchem/feat/tests/test_smiles_tokenizer.py +15 −8 Original line number Diff line number Diff line Loading @@ -8,15 +8,22 @@ from transformers import RobertaForMaskedLM class TestSmilesTokenizer(TestCase): """Tests the SmilesTokenizer to load the USPTO vocab file and a ChemBERTa Masked LM model with pre-trained weights..""" def test_tokenize(self): current_dir = os.path.dirname(os.path.realpath(__file__)) vocab_path = os.path.join(current_dir, 'data', 'vocab.txt') tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13] tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13] model = RobertaForMaskedLM.from_pretrained('seyonec/SMILES_tokenized_PubChem_shard00_50k') model = RobertaForMaskedLM.from_pretrained( 'seyonec/SMILES_tokenized_PubChem_shard00_50k') model.num_parameters() tokenizer = SmilesTokenizer(vocab_path, max_len=model.config.max_position_embeddings) tokenizer = SmilesTokenizer( vocab_path, max_len=model.config.max_position_embeddings) assert tokenized_smiles == tokenizer.encode("CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1") assert tokenized_smiles == tokenizer.encode( "CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1")
