language_model_template1.md

metadata

'[object Object]': null

{% set lm_task_entries = { 'text-generation': { 'direct_use': "The model can be used for text generation.", 'downstream_use': "To learn more about this task and potential downstream uses, see the Hugging Face text generation docs", 'misuse': "The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model." }, 'question-answering': { 'direct_use': "The model can be used for question answering.", 'downstream_use': "Potential types of question answering include extractive QA, open generative QA, and closed generative QA. To learn more about this task and potential downstream uses, see the Hugging Face question answering docs", 'misuse': "The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model." }, 'fill-mask': { 'direct_use': "The model can be used for masked language modeling.", 'downstream_use': "Masked language modeling are sometimes used to train large models for domain-specific problems. To learn more about this task and potential downstream uses, see the Hugging Face fill mask docs", 'misuse': "The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model." }, 'sentence_similarity': { 'direct_use': "The model can be used for sentence similarity, the task of determining how similar two texts are.", 'downstream_use': "Potential downstream use cases may include information retreival and clustering or grouping. To learn more about sentence similarity and potential downstream uses, see the Hugging Face sentence similarity docs", 'misuse': "" }, 'summarization': { 'direct_use': "The model can be used for summarization.", 'downstream_use': "To learn more about summarization and potential downstream uses, see the Hugging Face summarization docs.", 'misuse': "The model was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model." }, 'text_classification': { 'direct_use': "The model can be used for text classification, the task of assigning a label or class to a given text.", 'downstream_use': "Potential downstream use cases include sentiment analysis, natural language inference, and assessing grammatical correctness. To learn more about text classification and other potential downstream uses, see the Hugging Face text classification docs.", 'misuse': "" }, 'token_classification': { 'direct_use': "The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.", 'downstream_use': "Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face token classification docs.", 'misuse': "" }, 'translation': { 'direct_use': "The model can be used for translation, the task of converting text from one language to another.", 'downstream_use': "Potential downstream use cases include use cases that leverage conversational agents across different languages. To learn more about translation and other potential downstream use cases, see the Hugging Face translation docs.", 'misuse': "" }, } %}

{% set task_list = [ 'text_generation', 'question_answering', 'fill_mask', 'sentence_similarity', 'summarization', 'text_classification', 'token_classification', 'translation' ] %}

Model Card for {{ model_id }}

{{ the_model_description }}

{% if model_card_user == "policymaker" %}

Click to expand policymaker version of model card

Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Model Examination
5. Environmental Impact
6. Citation
7. Glossary
8. More Information
9. Model Card Authors
10. Model Card Contact

{% endif %}

Table of Contents

• Model Card for {{ model_id }}
• Table of Contents
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Environmental Impact
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Glossary [optional]
• More Information [optional]
• Model Card Authors [optional]
• Model Card Contact
• How to Get Started with the Model

Model Details

Model Description

{{ the_model_description }}

• Developed by: {{ developers | join(', ') | default("More information needed", true)}}
• Shared by [Optional]: {{ shared_by | join(', ') | default("More information needed", true)}}
• Model type: {{ model_type | default("Language model", true)}}
• Language(s) (NLP): {{ language | join(', ') | default("More information needed", true)}}
• License: {{ model_license | default("More information needed", true)}}
• Parent Model: {{ " Parent Model".format(repo_link) if parent_model_link else "More information needed"}}
• Resources for more information: {{ more_resources | default("More information needed", true)}} {{ " - GitHub Repo".format(repo_link) if repo_link}} {{ " - Associated Paper".format(paper_link) if paper_link }}

Uses

Direct Use

{% if direct_use is defined %} {{ direct_use }} {% elif model_task in task_list %} {{ lm_task_entries[model_task]['direct_use'] }} {% else %} More information needed. {% endif %}

Downstream Use [Optional]

{% if downstream_use is defined %} {{ downstream_use }} {% elif model_task in task_list %} {{ lm_task_entries[model_task]['downstream_use'] }} {% else %} More information needed. {% endif %}

Out-of-Scope Use

{% if out_of_scope_use is defined %} {{ out_of_scope_use }} {% elif model_task in task_list %} The model should not be used to intentionally create hostile or alienating environments for people. {{ lm_task_entries[model_task]['misuse'] }} {% else %} More information needed. {% endif %}

Bias, Risks, and Limitations

{% if bias_risks_limiations is defined %} {{ bias_risks_limitations }} {% else %} Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. {% endif %}

Recommendations

{% if bias_recommendations is defined %} {{ bias_recommendations }} {% else %} Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. {% endif %}

Training Details

Training Data

{{ training_data | default("More information on training data needed", true)}} {{ "See the associated dataset card for further details.".format(training_datacard_link) if training_data_card_link }}

Training Procedure

Preprocessing

{{ preprocessing | default("More information needed", true)}}

Speeds, Sizes, Times

{{ speeds_sizes_times | default("More information needed", true)}}

Evaluation

Testing Data, Factors & Metrics

Testing Data

{{ testing_data | default("More information needed", true)}} {{ "See the associated dataset card for further details.".format(testing_datacard_link) if testing_data_card_link }}

Factors

{{ testing_factors | default("More information needed", true)}}

Metrics

{{ testing_metrics | default("More information needed", true)}}

Results

{{ results | default("More information needed", true)}}

Model Examination

{{ model_examination | default("More information needed", true)}}

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: {{ hardware | default("More information needed", true)}}
• Hours used: {{ hours_used | default("More information needed", true)}}
• Cloud Provider: {{ cloud_provider | default("More information needed", true)}}
• Compute Region: {{ cloud_region | default("More information needed", true)}}
• Carbon Emitted: {{ co2_emitted | default("More information needed", true)}}

Technical Specifications [optional]

Model Architecture and Objective

{{ model_specs | default("More information needed", true)}}

Compute Infrastructure

{{ compute_infrastructure | default("More information needed", true)}}

Hardware

{{ hardware | default("More information needed", true)}}

Software

{{ software | default("More information needed", true)}}

Citation

BibTeX:

{{ citation_bibtex | default("More information needed", true)}}

APA:

{{ citation_apa | default("More information needed", true)}}

Glossary [optional]

{{ glossary | default("More information needed", true)}}

More Information [optional]

{{ more_information | default("More information needed", true)}}

Model Card Authors [optional]

{{ model_card_authors | default("More information needed", true)}}

Model Card Contact

{{ model_card_contact | default("More information needed", true)}}

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

{{ get_started_code | default("More information needed", true)}}