Instructions to use Taykhoom/BERT-updated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/BERT-updated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Taykhoom/BERT-updated", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Taykhoom/BERT-updated", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_bert.py with huggingface_hub
Browse files- modeling_bert.py +4 -3
modeling_bert.py
CHANGED
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@@ -271,12 +271,13 @@ class BertModel(PreTrainedModel):
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base_model_prefix = "bert"
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_supports_sdpa = True
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_supports_flash_attn_2 = True
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-
def __init__(self, config):
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super().__init__(config)
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self.embeddings = BertEmbeddings(config)
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self.encoder = BertEncoder(config)
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-
self.pooler = BertPooler(config)
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self.post_init()
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def get_input_embeddings(self):
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@@ -310,7 +311,7 @@ class BertModel(PreTrainedModel):
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output_hidden_states=output_hidden_states,
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output_attentions=output_attentions,
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)
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-
pooled = self.pooler(last_hidden_state)
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if not return_dict:
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return tuple(v for v in [last_hidden_state, pooled, all_hidden_states, all_attentions] if v is not None)
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base_model_prefix = "bert"
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_supports_sdpa = True
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_supports_flash_attn_2 = True
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+
_keys_to_ignore_on_load_missing = [r"pooler\."]
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+
def __init__(self, config, add_pooling_layer=True):
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super().__init__(config)
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self.embeddings = BertEmbeddings(config)
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self.encoder = BertEncoder(config)
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self.pooler = BertPooler(config) if add_pooling_layer else None
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self.post_init()
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def get_input_embeddings(self):
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output_hidden_states=output_hidden_states,
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output_attentions=output_attentions,
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)
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+
pooled = self.pooler(last_hidden_state) if self.pooler is not None else None
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if not return_dict:
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return tuple(v for v in [last_hidden_state, pooled, all_hidden_states, all_attentions] if v is not None)
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