AI model creator details for m3hrdadfi

distilbert-zwnj-wnli-mean-tokens

The distilbert-zwnj-wnli-mean-tokens model is a language model that is trained on the WNLI dataset for the task of sentence similarity. It uses the DistilBERT architecture to encode sentences into fixed-length embeddings, and then computes the mean of the embeddings to obtain a representation for the sentence pair. This model can be used for tasks such as paraphrase detection, natural language inference, and sentence similarity.

$-/run

292.4K

Huggingface

hubert-base-persian-speech-gender-recognition

m3hrdadfi

hubert-base-persian-speech-gender-recognition is a model for gender recognition in Persian speech. It is based on the HUBERT architecture, which is a hierarchical transformer model for speech recognition and language understanding tasks. The model takes in Persian speech data as input and predicts the gender of the speaker. It is trained on a large dataset of labeled speech data and achieves high accuracy in gender recognition.

$-/run

16.6K

Huggingface

typo-detector-distilbert-en

m3hrdadfi

The typo-detector-distilbert-en model is a token classification model that is trained to detect typos in English text. Given a sequence of input tokens, the model outputs a sequence of labels indicating whether each token is a typo or not. This can be useful for applications such as spell checkers or typo correctors. The model is based on the DistilBERT architecture, which is a smaller, faster version of the BERT model.

$-/run

6.0K

Huggingface

wav2vec2-xlsr-persian-speech-emotion-recognition

m3hrdadfi

Platform did not provide a description for this model.

$-/run

1.8K

Huggingface

bert-fa-base-uncased-wikinli-mean-tokens