size_categories:1K<n<10K format:parquet library:datasets library:pandas library:mlcroissant library:polars region:us
---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
dataset_info:
  features:
  - name: input_ids
    sequence: int32
  - name: attention_mask
    sequence: int8
  - name: labels
    dtype: int64
  splits:
  - name: train
    num_bytes: 10366704
    num_examples: 7999
  - name: val
    num_bytes: 2592000
    num_examples: 2000
  download_size: 575509
  dataset_size: 12958704
---
# Dataset Card for "covid-tweet-sentiment-analyzer-roberta-latest-data"


1. **input_ids:**
   - `input_ids` represent the input to a natural language processing (NLP) model in the form of tokenized and numerical values.
   - These are the tokenized versions of the text data, where words and tokens are converted to unique numerical identifiers.
   - These numerical values enable the model to understand and process the text data, making it suitable for machine learning algorithms.

2. **attention_mask:**
   - `attention_mask` is a companion to `input_ids` and is used to indicate which parts of the input sequence should be attended to by the model and which parts should be ignored.
   - The attention mask is important for maintaining the structure and integrity of the input data while accommodating variations in text length.

3. **labels:**
   - `labels` refer to the target values that the model is trying to predict.
   - These are '1' for neutral, '2' for positive, and '0' for negative sentiment.