# AutoTrain Dataset for project: dataset-mentions

## Dataset Description

This dataset has been automatically processed by AutoTrain for project dataset-mentions.

### Languages

The BCP-47 code for the dataset's language is en.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "text": " How to use  ```python from transformers import AutoTokenizer, AutoModel  tokenizer = AutoTokenizer.from_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\") model = AutoModel.from_pretrained(\"Geotrend/bert-base-en-fr-zh-ja-vi-cased\")  ```  To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).  ",
    "target": 0
  },
  {
    "text": " Training hyperparameters  The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3  ",
    "target": 1
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "text": "Value(dtype='string', id=None)",
  "target": "ClassLabel(names=['dataset_mention', 'no_dataset_mention'], id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 7428 |
| valid        | 1858 |

task_categories:text-classification language:en region:us
{"language": ["en"], "task_categories": ["text-classification"]}
TAGS
#task_categories-text-classification #language-English #region-us 

AutoTrain Dataset for project: dataset-mentions
===============================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project dataset-mentions.


### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased")


### Dataset Fields


The dataset has the following fields (also called "features"):


### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:




### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows:


python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#task_categories-text-classification #language-English #region-us 
 ### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows:


python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#task_categories-text-classification #language-English #region-us 
### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:


python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased") model = AutoModel.from\_pretrained("Geotrend/bert-base-en-fr-zh-ja-vi-cased")### Dataset Fields


The dataset has the following fields (also called "features"):### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow: