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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** None tokens |
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- **Output Dimensionality:** None tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): ConcatCustomPooling( |
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(model): BertModel( |
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(embeddings): BertEmbeddings( |
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(word_embeddings): Embedding(30522, 1024, padding_idx=0) |
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(position_embeddings): Embedding(512, 1024) |
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(token_type_embeddings): Embedding(2, 1024) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(encoder): BertEncoder( |
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(layer): ModuleList( |
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(0): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(1): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(2): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(3): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(4): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(5): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(6): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(7): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(8): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
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(output): BertOutput( |
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(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(9): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(10): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
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(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(11): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
|
) |
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(output): BertSelfOutput( |
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(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
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(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(12): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
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(query): Linear(in_features=1024, out_features=1024, bias=True) |
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(key): Linear(in_features=1024, out_features=1024, bias=True) |
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(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
|
(intermediate): BertIntermediate( |
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(dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
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(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(13): BertLayer( |
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(attention): BertAttention( |
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(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
|
(14): BertLayer( |
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(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(15): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(16): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(17): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(18): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(19): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(20): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(21): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(22): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(23): BertLayer( |
|
(attention): BertAttention( |
|
(self): BertSelfAttention( |
|
(query): Linear(in_features=1024, out_features=1024, bias=True) |
|
(key): Linear(in_features=1024, out_features=1024, bias=True) |
|
(value): Linear(in_features=1024, out_features=1024, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): BertSelfOutput( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): BertIntermediate( |
|
(dense): Linear(in_features=1024, out_features=4096, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): BertOutput( |
|
(dense): Linear(in_features=4096, out_features=1024, bias=True) |
|
(LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
) |
|
) |
|
(pooler): BertPooler( |
|
(dense): Linear(in_features=1024, out_features=1024, bias=True) |
|
(activation): Tanh() |
|
) |
|
) |
|
) |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Tomor0720/bge_large_en_v1.5_custom_pooling") |
|
# Run inference |
|
sentences = [ |
|
'The weather is lovely today.', |
|
"It's so sunny outside!", |
|
'He drove to the stadium.', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
</details> |
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|
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|
### Downstream Usage (Sentence Transformers) |
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|
You can finetune this model on your own dataset. |
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|
<details><summary>Click to expand</summary> |
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|
</details> |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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|
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### Recommendations |
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|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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|
|
## Training Details |
|
|
|
### Framework Versions |
|
- Python: 3.9.18 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.45.1 |
|
- PyTorch: 1.13.0+cu117 |
|
- Accelerate: 0.20.3 |
|
- Datasets: 2.13.0 |
|
- Tokenizers: 0.20.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
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