# Copyright 2026 the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import AutoModel, AutoTokenizer, NomicBertConfig, is_torch_available from transformers.testing_utils import ( Expectations, require_torch, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( NomicBertForMaskedLM, NomicBertForSequenceClassification, NomicBertForTokenClassification, NomicBertModel, ) class NomicBertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=2048, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None next_sentence_label = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) next_sentence_label = ids_tensor([self.batch_size], 2) config = self.get_config() return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ) def get_config(self): """ Returns a tiny configuration by default. """ return NomicBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, use_cache=False, initializer_range=self.initializer_range, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ): model = NomicBertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output, None) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ): model = NomicBertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ): config.num_labels = self.num_labels model = NomicBertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ): config.num_labels = self.num_labels model = NomicBertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, next_sentence_label, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class NomicBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( NomicBertModel, NomicBertForMaskedLM, NomicBertForSequenceClassification, NomicBertForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": NomicBertModel, "fill-mask": NomicBertForMaskedLM, "text-classification": NomicBertForSequenceClassification, "token-classification": NomicBertForTokenClassification, "zero-shot": NomicBertForSequenceClassification, } if is_torch_available() else {} ) model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = NomicBertModelTester(self) self.config_tester = ConfigTester(self, config_class=NomicBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @require_torch class NomicBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding_v1_5(self): # TODO: remove revision model = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1.5", revision="refs/pr/57").to(torch_device) tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1.5", revision="refs/pr/57") sentences = ["Plants create oxygen.", "Photosynthesis is a process where plants create oxygen."] inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True).to(torch_device) with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((2, 13, 768)) self.assertEqual(output.shape, expected_shape) # fmt: off expected_slice = Expectations( { (None, None): torch.tensor( [ [ [1.7039e00, -4.5610e00, 1.5236e00], [1.8685e00, -3.6936e00, 1.6641e00], [5.3303e-01, -4.2081e00, 2.3375e00], ], [ [2.6867e-03, -3.7496e00, 9.0820e-01], [1.8297e-02, -3.3884e00, 3.5300e-01], [-1.4282e-01, -3.6776e00, -3.5079e-01], ], ] ), } ).get_expectation() # fmt: on torch.testing.assert_close(output[:, 1:4, 1:4].cpu().detach(), expected_slice, rtol=1e-3, atol=1e-3) @slow def test_inference_no_head_absolute_embedding_v1(self): # TODO: remove revision model = AutoModel.from_pretrained("nomic-ai/nomic-embed-text-v1", revision="refs/pr/34").to(torch_device) tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-text-v1", revision="refs/pr/34") sentences = ["Plants create oxygen.", "Photosynthesis is a process where plants create oxygen."] inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True).to(torch_device) with torch.no_grad(): output = model(**inputs)[0] expected_shape = torch.Size((2, 13, 768)) self.assertEqual(output.shape, expected_shape) # fmt: off expected_slice = Expectations( { (None, None): torch.tensor( [ [ [ 1.2961, -1.1757, 1.2094], [ 1.1350, 0.5400, 1.4580], [-0.2897, -0.5351, 2.0092], ], [ [-0.2866, -0.9786, 0.8613], [-0.3104, -0.3421, 0.4867], [-0.4336, -0.8528, -0.2509], ] ] ), } ).get_expectation() # fmt: on torch.testing.assert_close(output[:, 1:4, 1:4].cpu().detach(), expected_slice, rtol=1e-3, atol=1e-3)