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webapp/seed/Lib/site-packages/sentence_transformers/model_card_template.md

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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {{ card_data }}

{{ model_name if model_name else "Sentence Transformer model" }}

This is a sentence-transformers model{% if base_model %} finetuned from [{{ base_model }}](https://huggingface.co/{{ base_model }}){% else %} trained{% endif %}{% if train_datasets | selectattr("name") | list %} on {% if train_datasets | selectattr("name") | map(attribute="name") | join(", ") | length > 200 %}{{ train_datasets | length }}{% else %}the {% for dataset in (train_datasets | selectattr("name")) %}{% if dataset.id %}[{{ dataset.name if dataset.name else dataset.id }}](https://huggingface.co/datasets/{{ dataset.id }}){% else %}{{ dataset.name }}{% endif %}{% if not loop.last %}{% if loop.index == (train_datasets | selectattr("name") | list | length - 1) %} and {% else %}, {% endif %}{% endif %}{% endfor %}{% endif %} dataset{{"s" if train_datasets | selectattr("name") | list | length > 1 else ""}}{% endif %}. It maps sentences & paragraphs to a {{ output_dimensionality }}-dimensional dense vector space and can be used for {{ task_name }}.

Model Details

Model Description

  • Model Type: Sentence Transformer {% if base_model -%} {%- if base_model_revision -%}

{%- endif %}

  • Maximum Sequence Length: {{ model_max_length }} tokens
  • Output Dimensionality: {{ output_dimensionality }} dimensions
  • Similarity Function: {{ similarity_fn_name }} {% if train_datasets | selectattr("name") | list -%}
    • Training Dataset{{"s" if train_datasets | selectattr("name") | list | length > 1 else ""}}: {%- for dataset in (train_datasets | selectattr("name")) %} {%- if dataset.id %}
    • [{{ dataset.name if dataset.name else dataset.id }}](https://huggingface.co/datasets/{{ dataset.id }}) {%- else %}
    • {{ dataset.name }} {%- endif %} {%- endfor %} {%- else -%}

{%- endif %} {% if language -%} - Language{{"s" if language is not string and language | length > 1 else ""}}: {%- if language is string %} {{ language }} {%- else %} {% for lang in language -%} {{ lang }}{{ ", " if not loop.last else "" }} {%- endfor %} {%- endif %} {%- else -%} {%- endif %} {% if license -%} - License: {{ license }} {%- else -%} {%- endif %}

Model Sources

Full Model Architecture

{{ model_string }}

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

{% if not ir_model %} Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the {{ hf_emoji }} Hub
model = SentenceTransformer("{{ model_id | default('sentence_transformers_model_id', true) }}")
# Run inference
sentences = [
{%- for text in (predict_example or ["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."]) %}
    {{ "%r" | format(text) }},
{%- endfor %}
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [{{ (predict_example or ["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."]) | length}}, {{ output_dimensionality | default(1024, true) }}]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
{% if similarities %}print(similarities)
{{ similarities }}{% else %}print(similarities.shape)
# [{{ (predict_example or ["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."]) | length}}, {{ (predict_example or ["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."]) | length}}]{% endif %}

{% else %} Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the {{ hf_emoji }} Hub
model = SentenceTransformer("{{ model_id | default('sentence_transformers_model_id', true) }}")
# Run inference
queries = [
    {{ predict_example | first | tojson }},
]
documents = [
{%- for text in predict_example[1:] %}
    {{ "%r" | format(text) }},
{%- endfor %}
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, {{ output_dimensionality | default(1024, true) }}] [{{ (predict_example | length) - 1 }}, {{ output_dimensionality | default(1024, true) }}]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
{% if similarities %}print(similarities)
{{ similarities }}{% else %}print(similarities.shape)
# [1, {{ (predict_example | length) - 1 }}]{% endif %}

{% endif %}

{% if eval_metrics %}

Evaluation

Metrics

{% for metrics in eval_metrics %}

{{ metrics.description }}

{% if metrics.dataset_name %}

  • Dataset{% if metrics.dataset_name is not string and metrics.dataset_name | length > 1 %}s{% endif %}: {% if metrics.dataset_name is string -%} {{ metrics.dataset_name }} {%- else -%} {%- for name in metrics.dataset_name -%} {{ name }} {%- if not loop.last -%} {%- if loop.index == metrics.dataset_name | length - 1 %} and {% else -%}, {% endif -%} {%- endif -%} {%- endfor -%} {%- endif -%} {%- endif %}
  • Evaluated with {% if metrics.class_name.startswith("sentence_transformers.") %}[{{ metrics.class_name.split(".")[-1] }}](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.{{ metrics.class_name.split(".")[-1] }}){% else %}{{ metrics.class_name }}{% endif %}{% if metrics.config_code %} with these parameters: {{ metrics.config_code }}{% endif %}

{{ metrics.table }} {%- endfor %}{% endif %}

Training Details

{% for dataset_type, dataset_list in [("training", train_datasets), ("evaluation", eval_datasets)] %}{% if dataset_list %}

{{ dataset_type.title() }} Dataset{{"s" if dataset_list | length > 1 else ""}}

{% for dataset in dataset_list %}{% if dataset_list | length > 3 %}

{{ dataset['name'] or 'Unnamed Dataset' }} {% endif %}

{{ dataset['name'] or 'Unnamed Dataset' }}

{% if dataset['name'] %}

  • Dataset: {% if 'id' in dataset %}[{{ dataset['name'] }}](https://huggingface.co/datasets/{{ dataset['id'] }}){% else %}{{ dataset['name'] }}{% endif %} {%- if 'revision' in dataset and 'id' in dataset %} at [{{ dataset['revision'][:7] }}](https://huggingface.co/datasets/{{ dataset['id'] }}/tree/{{ dataset['revision'] }}){% endif %}{% endif %} {% if dataset['size'] %}* Size: {{ "{:,}".format(dataset['size']) }} {{ dataset_type }} samples {% endif %}* Columns: {% if dataset['columns'] | length == 1 %}{{ dataset['columns'][0] }}{% elif dataset['columns'] | length == 2 %}{{ dataset['columns'][0] }} and {{ dataset['columns'][1] }}{% else %}{{ dataset['columns'][:-1] | join(', ') }}, and {{ dataset['columns'][-1] }}{% endif %} {% if dataset['stats_table'] %}* Approximate statistics based on the first {{ [dataset['size'], 1000] | min }} samples: {{ dataset['stats_table'] }}{% endif %}{% if dataset['examples_table'] %}* Samples: {{ dataset['examples_table'] }}{% endif %}* Loss: {% if dataset["loss"]["fullname"].startswith("sentence_transformers.") %}[{{ dataset["loss"]["fullname"].split(".")[-1] }}](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#{{ dataset["loss"]["fullname"].split(".")[-1].lower() }}){% else %}{{ dataset["loss"]["fullname"] }}{% endif %}{% if "config_code" in dataset["loss"] %} with these parameters: {{ dataset["loss"]["config_code"] }}{% endif %} {% if dataset_list | length > 3 %}
{% endif %}{% endfor %}{% endif %}{% endfor -%}

{% if all_hyperparameters %}

Training Hyperparameters

{% if non_default_hyperparameters -%}

Non-Default Hyperparameters

{% for name, value in non_default_hyperparameters.items() %}- {{ name }}: {{ value }} {% endfor %}{%- endif %}

All Hyperparameters

Click to expand

{% for name, value in all_hyperparameters.items() %}- {{ name }}: {{ value }} {% endfor %}

{% endif %}

{%- if eval_lines %}

Training Logs

{% if hide_eval_lines %}

Click to expand

{% endif -%} {{ eval_lines }}{% if explain_bold_in_eval %}

  • The bold row denotes the saved checkpoint.{% endif %} {%- if hide_eval_lines %}
{% endif %} {% endif %}

{%- if co2_eq_emissions %}

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Energy Consumed: {{ "%.3f"|format(co2_eq_emissions["energy_consumed"]) }} kWh
  • Carbon Emitted: {{ "%.3f"|format(co2_eq_emissions["emissions"] / 1000) }} kg of CO2
  • Hours Used: {{ co2_eq_emissions["hours_used"] }} hours

Training Hardware

  • On Cloud: {{ "Yes" if co2_eq_emissions["on_cloud"] else "No" }}
  • GPU Model: {{ co2_eq_emissions["hardware_used"] or "No GPU used" }}
  • CPU Model: {{ co2_eq_emissions["cpu_model"] }}
  • RAM Size: {{ "%.2f"|format(co2_eq_emissions["ram_total_size"]) }} GB {% endif %}

Framework Versions

  • Python: {{ version["python"] }}
  • Sentence Transformers: {{ version["sentence_transformers"] }}
  • Transformers: {{ version["transformers"] }}
  • PyTorch: {{ version["torch"] }}
  • Accelerate: {{ version["accelerate"] }}
  • Datasets: {{ version["datasets"] }}
  • Tokenizers: {{ version["tokenizers"] }}

Citation

BibTeX

{% for loss_name, citation in citations.items() %}

{{ loss_name }}

{{ citation | trim }}

{% endfor %}