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Expand Up @@ -41,69 +41,69 @@ This is an overview of the tasks supported by the AllenNLP Models library along

- [**Classification**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/classification)

Classification tasks involve predicting one or more labels from a predefined set to assign to each input. Examples include Sentiment Analysis, where the labels might be `{"positive", "negative", "neutral"}`, and Binary Question Answering, where the labels are `{True, False}`.
Classification tasks involve predicting one or more labels from a predefined set to assign to each input. Examples include Sentiment Analysis, where the labels might be `{"positive", "negative", "neutral"}`, and Binary Question Answering, where the labels are `{True, False}`.

🛠 **Components provided:** Dataset readers for various datasets, including [BoolQ](https://docs.allennlp.org/models/main/models/classification/dataset_readers/boolq/) and [SST](https://docs.allennlp.org/models/main/models/classification/dataset_readers/stanford_sentiment_tree_bank/), as well as a [Biattentive Classification Network](https://docs.allennlp.org/models/main/models/classification/models/biattentive_classification_network/) model.
🛠 **Components provided:** Dataset readers for various datasets, including [BoolQ](https://docs.allennlp.org/models/main/models/classification/dataset_readers/boolq/) and [SST](https://docs.allennlp.org/models/main/models/classification/dataset_readers/stanford_sentiment_tree_bank/), as well as a [Biattentive Classification Network](https://docs.allennlp.org/models/main/models/classification/models/biattentive_classification_network/) model.

- [**Coreference Resolution**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/coref)

Coreference resolution tasks require finding all of the expressions in a text that refer to common entities.
Coreference resolution tasks require finding all of the expressions in a text that refer to common entities.

<div align="center">
<a href="https://nlp.stanford.edu/projects/coref.shtml"><img src="https://nlp.stanford.edu/projects/corefexample.png" width="300" /></a>
</div>
<div align="center">
<a href="https://nlp.stanford.edu/projects/coref.shtml"><img src="https://nlp.stanford.edu/projects/corefexample.png" width="300" /></a>
</div>

See [nlp.stanford.edu/projects/coref](https://nlp.stanford.edu/projects/coref.shtml) for more details.
See [nlp.stanford.edu/projects/coref](https://nlp.stanford.edu/projects/coref.shtml) for more details.

🛠 **Components provided:** A general [Coref](https://docs.allennlp.org/models/main/models/coref/models/coref/) model and several dataset readers.
🛠 **Components provided:** A general [Coref](https://docs.allennlp.org/models/main/models/coref/models/coref/) model and several dataset readers.

- [**Generation**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/generation)

This is a broad category for tasks such as Summarization that involve generating unstructered and often variable-length text.
This is a broad category for tasks such as Summarization that involve generating unstructered and often variable-length text.

🛠 **Components provided:** Several Seq2Seq models such a [Bart](https://docs.allennlp.org/models/main/models/generation/models/bart/), [CopyNet](https://docs.allennlp.org/models/main/models/generation/models/copynet_seq2seq/), and a general [Composed Seq2Seq](https://docs.allennlp.org/models/main/models/generation/models/copynet_seq2seq/), along with corresponding dataset readers.
🛠 **Components provided:** Several Seq2Seq models such a [Bart](https://docs.allennlp.org/models/main/models/generation/models/bart/), [CopyNet](https://docs.allennlp.org/models/main/models/generation/models/copynet_seq2seq/), and a general [Composed Seq2Seq](https://docs.allennlp.org/models/main/models/generation/models/copynet_seq2seq/), along with corresponding dataset readers.

- [**Language Modeling**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/lm)

Language modeling tasks involve learning a probability distribution over sequences of tokens.
Language modeling tasks involve learning a probability distribution over sequences of tokens.

🛠 **Components provided:** Several language model implementations, such as a [Masked LM](https://docs.allennlp.org/models/main/models/lm/models/masked_language_model/) and a [Next Token LM](https://docs.allennlp.org/models/main/models/lm/models/next_token_lm/).
🛠 **Components provided:** Several language model implementations, such as a [Masked LM](https://docs.allennlp.org/models/main/models/lm/models/masked_language_model/) and a [Next Token LM](https://docs.allennlp.org/models/main/models/lm/models/next_token_lm/).

- [**Multiple Choice**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/mc)

Multiple choice tasks require selecting a correct choice among alternatives, where the set of choices may be different for each input. This differs from classification where the set of choices is predefined and fixed across all inputs.
Multiple choice tasks require selecting a correct choice among alternatives, where the set of choices may be different for each input. This differs from classification where the set of choices is predefined and fixed across all inputs.

🛠 **Components provided:** A [transformer-based multiple choice model](https://docs.allennlp.org/models/main/models/mc/models/transformer_mc/) and a handful of dataset readers for specific datasets.
🛠 **Components provided:** A [transformer-based multiple choice model](https://docs.allennlp.org/models/main/models/mc/models/transformer_mc/) and a handful of dataset readers for specific datasets.

- [**Pair Classification**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/pair_classification)

Pair classification is another broad category that contains tasks such as Textual Entailment, which is to determine whether, for a pair of sentences, the facts in the first sentence imply the facts in the second.
Pair classification is another broad category that contains tasks such as Textual Entailment, which is to determine whether, for a pair of sentences, the facts in the first sentence imply the facts in the second.

🛠 **Components provided:** Dataset readers for several datasets, including [SNLI](https://docs.allennlp.org/models/main/models/pair_classification/dataset_readers/snli/) and [Quora Paraphrase](https://docs.allennlp.org/models/main/models/pair_classification/dataset_readers/quora_paraphrase/).
🛠 **Components provided:** Dataset readers for several datasets, including [SNLI](https://docs.allennlp.org/models/main/models/pair_classification/dataset_readers/snli/) and [Quora Paraphrase](https://docs.allennlp.org/models/main/models/pair_classification/dataset_readers/quora_paraphrase/).

- [**Reading Comprehension**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/rc)

Reading comprehension tasks involve answering questions about a passage of text to show that the system understands the passage.
Reading comprehension tasks involve answering questions about a passage of text to show that the system understands the passage.

🛠 **Components provided:** Models such as [BiDAF](https://docs.allennlp.org/models/main/models/rc/models/bidaf/) and a [transformer-based QA model](https://docs.allennlp.org/models/main/models/rc/models/transformer_qa/), as well as readers for datasets such as [DROP](https://docs.allennlp.org/models/main/models/rc/dataset_readers/drop/), [QuAC](https://docs.allennlp.org/models/main/models/rc/dataset_readers/quac/), and [SQuAD](https://docs.allennlp.org/models/main/models/rc/dataset_readers/squad/).
🛠 **Components provided:** Models such as [BiDAF](https://docs.allennlp.org/models/main/models/rc/models/bidaf/) and a [transformer-based QA model](https://docs.allennlp.org/models/main/models/rc/models/transformer_qa/), as well as readers for datasets such as [DROP](https://docs.allennlp.org/models/main/models/rc/dataset_readers/drop/), [QuAC](https://docs.allennlp.org/models/main/models/rc/dataset_readers/quac/), and [SQuAD](https://docs.allennlp.org/models/main/models/rc/dataset_readers/squad/).

- [**Structured Prediction**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/structured_prediction)

Structured prediction includes tasks such as Semantic Role Labeling (SRL), which is for determining the latent predicate argument structure of a sentence and providing representations that can answer basic questions about sentence meaning, including who did what to whom, etc.
Structured prediction includes tasks such as Semantic Role Labeling (SRL), which is for determining the latent predicate argument structure of a sentence and providing representations that can answer basic questions about sentence meaning, including who did what to whom, etc.

🛠 **Components provided:** Dataset readers for [Penn Tree Bank](https://docs.allennlp.org/models/main/models/structured_prediction/dataset_readers/penn_tree_bank/), [OntoNotes](https://docs.allennlp.org/models/main/models/structured_prediction/dataset_readers/srl/), etc., and several models including one for [SRL](https://docs.allennlp.org/models/main/models/structured_prediction/models/srl/) and a very general [graph parser](https://docs.allennlp.org/models/main/models/structured_prediction/models/graph_parser/).
🛠 **Components provided:** Dataset readers for [Penn Tree Bank](https://docs.allennlp.org/models/main/models/structured_prediction/dataset_readers/penn_tree_bank/), [OntoNotes](https://docs.allennlp.org/models/main/models/structured_prediction/dataset_readers/srl/), etc., and several models including one for [SRL](https://docs.allennlp.org/models/main/models/structured_prediction/models/srl/) and a very general [graph parser](https://docs.allennlp.org/models/main/models/structured_prediction/models/graph_parser/).

- [**Sequence Tagging**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/tagging)

Sequence tagging tasks include Named Entity Recognition (NER) and Fine-grained NER.
Sequence tagging tasks include Named Entity Recognition (NER) and Fine-grained NER.

🛠 **Components provided:** A [Conditional Random Field model](https://docs.allennlp.org/models/main/models/tagging/models/crf_tagger/) and dataset readers for datasets such as [CoNLL-2000](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/conll2000/), [CoNLL-2003](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/conll2003/), [CCGbank](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/ccgbank/), and [OntoNotes](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/ontonotes_ner/).
🛠 **Components provided:** A [Conditional Random Field model](https://docs.allennlp.org/models/main/models/tagging/models/crf_tagger/) and dataset readers for datasets such as [CoNLL-2000](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/conll2000/), [CoNLL-2003](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/conll2003/), [CCGbank](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/ccgbank/), and [OntoNotes](https://docs.allennlp.org/models/main/models/tagging/dataset_readers/ontonotes_ner/).

- [**Text + Vision**](https://github.com/allenai/allennlp-models/tree/main/allennlp_models/vision)

This is a catch-all category for any text + vision multi-modal tasks such Visual Question Answering (VQA), the task of generating a answer in response to a natural language question about the contents of an image.
This is a catch-all category for any text + vision multi-modal tasks such Visual Question Answering (VQA), the task of generating a answer in response to a natural language question about the contents of an image.

🛠 **Components provided:** Several models such as a [ViLBERT model for VQA](https://docs.allennlp.org/models/main/models/vision/models/vilbert_vqa/) and one for [Visual Entailment](https://docs.allennlp.org/models/main/models/vision/models/visual_entailment/), along with corresponding dataset readers.
🛠 **Components provided:** Several models such as a [ViLBERT model for VQA](https://docs.allennlp.org/models/main/models/vision/models/vilbert_vqa/) and one for [Visual Entailment](https://docs.allennlp.org/models/main/models/vision/models/visual_entailment/), along with corresponding dataset readers.

### Pre-trained models

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