# High-precision NLP

This is a transformers model hosted on Hugging Face Hub. Specific functions and uses require further information.

TookaBERT is a family of encoder models trained on Persian, including base and large versions, suitable for various natural language processing tasks.

This is a text generation model based on the Apache-2.0 license, with specific functions and uses to be supplemented.

A language acceptability classification model fine-tuned based on RuRoBERTa-large for judging grammatical correctness of Russian texts

A model fine-tuned based on roberta-large for specific task processing, with an evaluation loss value of 0.7825

This model is a fine-tuned version based on hfl/chinese-bert-wwm on an unknown dataset, suitable for Chinese text processing tasks.

This model is a fine-tuned version of the RoBERTa base model, specifically optimized for contract understanding tasks using the CUAD dataset.

This model is a fine-tuned version of DeBERTa v2 XLarge, specifically optimized for contract understanding tasks, trained using the CUAD dataset.

A fine-tuned model based on DistilBERT for the grammatical acceptability classification task, performing excellently on the evaluation set.

KLUE-RoBERTa-large is a Korean pre-trained language model based on the RoBERTa architecture, developed by a Korean research team and optimized for Korean natural language processing tasks.

This model is a fine-tuned version of DeBERTa-v3-small on the GLUE COLA dataset for linguistic acceptability judgment tasks.

This is a POS tagging model fine-tuned based on the Indonesian RoBERTa model, trained on the indonlu dataset for Indonesian text POS tagging tasks.

This model is fine-tuned from bert-base-uncased-sparse-70-unstructured on the MNLI task (GLUE benchmark), with the classifier layer removed for easier loading into other downstream tasks for training.