test_models/setfit/model_card_template.md

metadata

'[object Object]': null

{{ model_name if model_name else ( "SetFit Aspect Model for Aspect Based Sentiment Analysis" if is_aspect else ( "SetFit Polarity Model for Aspect Based Sentiment Analysis" if is_aspect is False else "SetFit Model for Text Classification"))}}

This is a SetFit model{% if dataset_id %} trained on the [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }}) dataset{% endif %} that can be used for {{ task_name | default("Text Classification", true) }}.{% if st_id %} This SetFit model uses [{{ st_id }}](https://huggingface.co/{{ st_id }}) as the Sentence Transformer embedding model.{% endif %} A {{ head_class }} instance is used for classification.{% if is_absa %} In particular, this model is in charge of {{ "filtering aspect span candidates" if is_aspect else "classifying aspect polarities"}}.{% endif %}

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.

2. Training a classification head with features from the fine-tuned Sentence Transformer. {% if is_absa %} This model was trained within the context of a larger system for ABSA, which looks like so:

3. Use a spaCy model to select possible aspect span candidates.

4. {{ "" if is_aspect else "" }}Use {{ "this" if is_aspect else "a" }} SetFit model to filter these possible aspect span candidates.{{ "" if is_aspect else "" }}

5. {{ "" if not is_aspect else "" }}Use {{ "this" if not is_aspect else "a" }} SetFit model to classify the filtered aspect span candidates.{{ "" if not is_aspect else "" }} {% endif %}

Model Details

Model Description

• Model Type: SetFit {% if st_id -%}
  • Sentence Transformer body: [{{ st_id }}](https://huggingface.co/{{ st_id }}) {%- else -%} {%- endif %} {% if head_class -%}
  • Classification head: a {{ head_class }} instance {%- else -%} {%- endif %} {%- if spacy_model %}
• spaCy Model: {{ spacy_model }} {%- endif %} {%- if aspect_model %}
• SetFitABSA Aspect Model: [{{ aspect_model }}](https://huggingface.co/{{ aspect_model }}) {%- endif %} {%- if polarity_model %}
• SetFitABSA Polarity Model: [{{ polarity_model }}](https://huggingface.co/{{ polarity_model }}) {%- endif %}
• Maximum Sequence Length: {{ model_max_length }} tokens {% if num_classes -%}
  • Number of Classes: {{ num_classes }} classes {%- else -%} {%- endif %} {% if dataset_id -%}
  • Training Dataset: [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }}) {%- 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

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts {% if label_examples %}

Model Labels

{{ label_examples }}{% endif -%} {% if metrics_table %}

Evaluation

Metrics

{{ metrics_table }}{% endif %}

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

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

from setfit import AbsaModel

# Download from the {{ hf_emoji }} Hub
model = AbsaModel.from_pretrained(
    "{{ aspect_model }}",
    "{{ polarity_model }}",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")

{%- else %}

from setfit import SetFitModel

# Download from the {{ hf_emoji }} Hub
model = SetFitModel.from_pretrained("{{ model_id | default('setfit_model_id', true) }}")
# Run inference
preds = model("{{ predict_example | default("I loved the spiderman movie!", true) | replace('"', '\\"') }}")

{%- endif %}

Training Details

{% if train_set_metrics %}

Training Set Metrics

{{ train_set_metrics }}{% if train_set_sentences_per_label_list %} {{ train_set_sentences_per_label_list }}{% endif %}{% endif %}{% if hyperparameters %}

Training Hyperparameters

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

Training Results

{{ eval_lines }}{% if explain_bold_in_eval %}

• The bold row denotes the saved checkpoint.{% endif %}{% endif %}{% if co2_eq_emissions %}

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• 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"] }}
• SetFit: {{ version["setfit"] }}
• Sentence Transformers: {{ version["sentence_transformers"] }} {%- if "spacy" in version %}
• spaCy: {{ version["spacy"] }} {%- endif %}
• Transformers: {{ version["transformers"] }}
• PyTorch: {{ version["torch"] }}
• Datasets: {{ version["datasets"] }}
• Tokenizers: {{ version["tokenizers"] }}

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}