
# Dataset Card for "SPECTER"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)
  
## Dataset Description

- **Homepage:** [https://github.com/allenai/specter](https://github.com/allenai/specter)
- **Repository:** [More Information Needed](https://github.com/allenai/specter/blob/master/README.md)
- **Paper:** [More Information Needed](https://arxiv.org/pdf/2004.07180.pdf)
- **Point of Contact:** [@armancohan](https://github.com/armancohan), [@sergeyf](https://github.com/sergeyf), [@haroldrubio](https://github.com/haroldrubio), [@jinamshah](https://github.com/jinamshah)

### Dataset Summary

Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.

Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. 
These steps were done by the Hugging Face team.

## Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".

Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):

```
{"set": [anchor, positive, negative]}
{"set": [anchor, positive, negative]}
...
{"set": [anchor, positive, negative]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.

### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/SPECTER")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
    train: Dataset({
        features: ['set'],
        num_rows: 684100
    })
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```

### Curation Rationale

[More Information Needed](https://github.com/allenai/specter)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/allenai/specter)

#### Who are the source language producers?

[More Information Needed](https://github.com/allenai/specter)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/allenai/specter)

#### Who are the annotators?

[More Information Needed](https://github.com/allenai/specter)

### Personal and Sensitive Information

[More Information Needed](https://github.com/allenai/specter)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/allenai/specter)

### Discussion of Biases

[More Information Needed](https://github.com/allenai/specter)

### Other Known Limitations

[More Information Needed](https://github.com/allenai/specter)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/allenai/specter)

### Licensing Information

[More Information Needed](https://github.com/allenai/specter)

### Citation Information



### Contributions


task_categories:sentence-similarity task_ids:semantic-similarity-classification language:en license:mit arxiv:2004.07180 region:us
{"language": ["en"], "license": "mit", "task_categories": ["sentence-similarity", "paraphrase-mining"], "task_ids": ["semantic-similarity-classification"], "paperswithcode_id": "embedding-data/SPECTER", "pretty_name": "SPECTER"}
TAGS
#task_categories-sentence-similarity #task_ids-semantic-similarity-classification #language-English #license-mit #arxiv-2004.07180 #region-us 


# Dataset Card for "SPECTER"

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions
  
## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Point of Contact: @armancohan, @sergeyf, @haroldrubio, @jinamshah

### Dataset Summary

Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.

Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. 
These steps were done by the Hugging Face team.

## Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".

Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):


This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.

### Usage Example
Install the  Datasets library with 'pip install datasets' and load the dataset from the Hub with:

The dataset is loaded as a 'DatasetDict' and has the format:

Review an example 'i' with:


### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions


# Dataset Card for "SPECTER" ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Point of Contact: @armancohan, @sergeyf, @haroldrubio, @jinamshah ### Dataset Summary

Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.

Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. 
These steps were done by the Hugging Face team. ## Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".

Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):


This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets. ### Usage Example
Install the  Datasets library with 'pip install datasets' and load the dataset from the Hub with:

The dataset is loaded as a 'DatasetDict' and has the format:

Review an example 'i' with: ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#task_categories-sentence-similarity #task_ids-semantic-similarity-classification #language-English #license-mit #arxiv-2004.07180 #region-us 
 # Dataset Card for "SPECTER" ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Point of Contact: @armancohan, @sergeyf, @haroldrubio, @jinamshah ### Dataset Summary

Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.

Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. 
These steps were done by the Hugging Face team. ## Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".

Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):


This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets. ### Usage Example
Install the  Datasets library with 'pip install datasets' and load the dataset from the Hub with:

The dataset is loaded as a 'DatasetDict' and has the format:

Review an example 'i' with: ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#task_categories-sentence-similarity #task_ids-semantic-similarity-classification #language-English #license-mit #arxiv-2004.07180 #region-us 
# Dataset Card for "SPECTER"## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Point of Contact: @armancohan, @sergeyf, @haroldrubio, @jinamshah### Dataset Summary

Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.

Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. 
These steps were done by the Hugging Face team.## Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".

Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):


This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.### Usage Example
Install the  Datasets library with 'pip install datasets' and load the dataset from the Hub with:

The dataset is loaded as a 'DatasetDict' and has the format:

Review an example 'i' with:### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process