test_models/setfit/model_card_template.md

---

# 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 ( "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](https://github.com/huggingface/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](https://www.sbert.net) 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:



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

2. {{ "**" if is_aspect else "" }}Use {{ "this" if is_aspect else "a" }} SetFit model to filter these possible aspect span candidates.{{ "**" if is_aspect else "" }}
3. {{ "**" 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 -%}
    <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->

{%- endif %}

{% if head_class -%}

    - **Classification head:** a {{ head_class }} instance

{%- else -%}

    <!-- - **Classification head:** Unknown -->

{%- 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 -%}

    <!-- - **Number of Classes:** Unknown -->

{%- endif %}

{% if dataset_id -%}

    - **Training Dataset:** [{{ dataset_name if dataset_name else dataset_id }}](https://huggingface.co/datasets/{{ dataset_id }})

{%- else -%}

    <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->

{%- 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 -%}

    <!-- - **Language:** Unknown -->

{%- endif %}

{% if license -%}

    - **License:** {{ license }}

{%- else -%}

    <!-- - **License:** Unknown -->

{%- endif %}


### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
{% 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:

```bash

pip install setfit

```

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

```python

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 %}

```python

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 %}



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### Downstream Use



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### Out-of-Scope Use



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## Bias, Risks and Limitations



*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*

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### Recommendations



*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*

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## 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](https://github.com/mlco2/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

```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}

}

```

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## Glossary

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