rosherd
/

job_classification

@@ -1,283 +1,95 @@
 ---
-language:
-- ru
-- en
 tags:
-- translation
 license: apache-2.0
 datasets:
-- wmt19
-metrics:
-- bleu
-- sacrebleu
 widget:
-- text: "Jens Peter Hansen kommer fra Danmark"
----
-# Model Card for Job Classification
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
-model to predict job class(es) from any of
-Marketing &#39;account_management&#39;, &#39;accounting&#39;, &#39;administration&#39;, &#39;aerospace&#39;,
-&#39;agriculture&#39;, &#39;automotive&#39;, &#39;banking&#39;, &#39;buying_purchashing&#39;,
-&#39;charity_volunteer&#39;, &#39;cleaning&#39;, &#39;construction&#39;, &#39;consulting&#39;, &#39;cosmetology&#39;,
-&#39;council&#39;, &#39;creative_media_design&#39;, &#39;customer_services&#39;, &#39;education&#39;, &#39;energy&#39;,
-&#39;engineering&#39;, &#39;environmental&#39;, &#39;financial_services&#39;, &#39;graduate&#39;, &#39;healthcare&#39;,
-&#39;holiday_seasonal&#39;, &#39;hospitality&#39;, &#39;human_resources&#39;, &#39;human_services&#39;,
-&#39;insurance&#39;, &#39;internship&#39;, &#39;it_telecoms&#39;, &#39;legal&#39;, &#39;management&#39;,
-&#39;manufacturing&#39;, &#39;military_defence&#39;, &#39;multilingual&#39;, &#39;oil_and_gas&#39;,
-&#39;project_management&#39;, &#39;purchasing&#39;, &#39;real_estate&#39;, &#39;recruitment&#39;, &#39;retail&#39;,
-&#39;sales&#39;, &#39;science&#39;, &#39;security_safety&#39;, &#39;sports_fitness&#39;, &#39;strategy_consultancy&#39;,
-&#39;summer&#39;, &#39;training&#39;, &#39;transport_logistics&#39;, &#39;travel_tourism&#39;,
-&#39;veterinary_animal_care&#39;, &#39;warehouse&#39;, &#39;work_from_home&#39; ```
-#  Table of Contents
-- [Model Card for Job Classification](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-# Model Details
-## Model Description
-<!-- Provide a longer summary of what this model is/does. -->
-model to predict job class(es) from any of
-Marketing &#39;account_management&#39;, &#39;accounting&#39;, &#39;administration&#39;, &#39;aerospace&#39;,
-&#39;agriculture&#39;, &#39;automotive&#39;, &#39;banking&#39;, &#39;buying_purchashing&#39;,
-&#39;charity_volunteer&#39;, &#39;cleaning&#39;, &#39;construction&#39;, &#39;consulting&#39;, &#39;cosmetology&#39;,
-&#39;council&#39;, &#39;creative_media_design&#39;, &#39;customer_services&#39;, &#39;education&#39;, &#39;energy&#39;,
-&#39;engineering&#39;, &#39;environmental&#39;, &#39;financial_services&#39;, &#39;graduate&#39;, &#39;healthcare&#39;,
-&#39;holiday_seasonal&#39;, &#39;hospitality&#39;, &#39;human_resources&#39;, &#39;human_services&#39;,
-&#39;insurance&#39;, &#39;internship&#39;, &#39;it_telecoms&#39;, &#39;legal&#39;, &#39;management&#39;,
-&#39;manufacturing&#39;, &#39;military_defence&#39;, &#39;multilingual&#39;, &#39;oil_and_gas&#39;,
-&#39;project_management&#39;, &#39;purchasing&#39;, &#39;real_estate&#39;, &#39;recruitment&#39;, &#39;retail&#39;,
-&#39;sales&#39;, &#39;science&#39;, &#39;security_safety&#39;, &#39;sports_fitness&#39;, &#39;strategy_consultancy&#39;,
-&#39;summer&#39;, &#39;training&#39;, &#39;transport_logistics&#39;, &#39;travel_tourism&#39;,
-&#39;veterinary_animal_care&#39;, &#39;warehouse&#39;, &#39;work_from_home&#39; ```
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
-- **Language(s) (NLP):** More information needed
-- **License:** More information needed
-- **Parent Model:** More information needed
-- **Resources for more information:** More information needed
-# Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-## Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-## Downstream Use [Optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-## Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-# Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-## Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-# Training Details
-## Training Data
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-More information on training data needed
-## Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-### Preprocessing
-More information needed
-### Speeds, Sizes, Times
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-More information needed
-# Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-## Testing Data, Factors & Metrics
-### Testing Data
-<!-- This should link to a Data Card if possible. -->
-More information needed
-### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-More information needed
-### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-More information needed
-## Results
-More information needed
-# Model Examination
-More information needed
-# Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** More information needed
-- **Hours used:** More information needed
-- **Cloud Provider:** More information needed
-- **Compute Region:** More information needed
-- **Carbon Emitted:** More information needed
-# Technical Specifications [optional]
-## Model Architecture and Objective
-More information needed
-## Compute Infrastructure
-More information needed
-### Hardware
-More information needed
-### Software
-More information needed
-# Citation
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-More information needed
-**APA:**
-More information needed
-# Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-More information needed
-# More Information [optional]
-More information needed
-# Model Card Authors [optional]
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
-D, a, n,  , R, o, s, h, e, r
-# Model Card Contact
-More information needed
-# How to Get Started with the Model
-Use the code below to get started with the model.
-<details>
-<summary> Click to expand </summary>
-More information needed
-</details>

 ---
+language: en
 tags:
+- exbert
 license: apache-2.0
 datasets:
+- bookcorpus
+- wikipedia
 widget:
+- text: "Paris is the <mask> of France."
+  example_title: "Capital"
+- text: "The goal of life is"
+  example_title: "Philosophy"
+---
+# Job Classification
+This model will predict class(es) from any of
+```
+['Marketing',
+ 'account_management',
+ 'accounting',
+ 'administration',
+ 'aerospace',
+ 'agriculture',
+ 'automotive',
+ 'banking',
+ 'buying_purchashing',
+ 'charity_volunteer',
+ 'cleaning',
+ 'construction',
+ 'consulting',
+ 'cosmetology',
+ 'council',
+ 'creative_media_design',
+ 'customer_services',
+ 'education',
+ 'energy',
+ 'engineering',
+ 'environmental',
+ 'financial_services',
+ 'graduate',
+ 'healthcare',
+ 'holiday_seasonal',
+ 'hospitality',
+ 'human_resources',
+ 'human_services',
+ 'insurance',
+ 'internship',
+ 'it_telecoms',
+ 'legal',
+ 'management',
+ 'manufacturing',
+ 'military_defence',
+ 'multilingual',
+ 'oil_and_gas',
+ 'project_management',
+ 'purchasing',
+ 'real_estate',
+ 'recruitment',
+ 'retail',
+ 'sales',
+ 'science',
+ 'security_safety',
+ 'sports_fitness',
+ 'strategy_consultancy',
+ 'summer',
+ 'training',
+ 'transport_logistics',
+ 'travel_tourism',
+ 'veterinary_animal_care',
+ 'warehouse',
+ 'work_from_home']
+```
+## Model description
+As above
+## Training data
+pretrained on RL job data
+## Evaluation results
+When fine-tuned on downstream tasks, this model achieves the following results:
+Glue test results:
+| Task | MNLI | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  |
+|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
+|      | 82.2 | 88.5 | 89.2 | 91.3  | 51.3 | 85.8  | 87.5 | 59.9 |