AI model creator details for hackathon-pln-es

wav2vec2-base-finetuned-sentiment-classification-MESD

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

2.1K

Huggingface

paraphrase-spanish-distilroberta

hackathon-pln-es

paraphrase-spanish-distilroberta This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. We follow a teacher-student transfer learning approach to train an bertin-roberta-base-spanish model using parallel EN-ES sentence pairs. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Full Model Architecture Evaluation Results Similarity Evaluation on STS-2017.es-en.txt and STS-2017.es-es.txt (translated manually for evaluation purposes) We measure the semantic textual similarity (STS) between sentence pairs in different languages: ES-ES ES-EN Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. Background This model is a bilingual Spanish-English model trained according to instructions in the paper Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation and the documentation accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder (paraphrase-mpnet-base-v2) as a teacher model, and the pretrained Spanish BERTIN as the student model. We developped this model during the Hackathon 2022 NLP - Spanish, organized by hackathon-pln-es Organization. Training data We use the concatenation from multiple datasets with sentence pairs (EN-ES). We could check out the dataset that was used during training: parallel-sentences Authors Anibal Pérez, Emilio Tomás Ariza, Lautaro Gesuelli Pinto Mauricio Mazuecos

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1.0K

Huggingface

bertin-roberta-base-zeroshot-esnli

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

81

Huggingface

t5-small-spanish-nahuatl

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

76

Huggingface

bertin-roberta-base-finetuning-esnli

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

67

Huggingface

jurisbert-clas-art-convencion-americana-dh

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

64

Huggingface

t5-small-finetuned-spanish-to-quechua

hackathon-pln-es

Spanish to Quechua translator This model is a finetuned version of the t5-small. Model description t5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the test. Intended uses & limitations A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences. How to use You can import this model as follows: To translate you can do: Limitations and bias Actually this model only can translate to Quechua of Ayacucho. Training data For train this model we use Spanish to Quechua dataset Evaluation results We obtained the following metrics during the training process: eval_bleu = 2.9691 eval_loss = 1.2064628601074219 Team members Sara Benel Jose Vílchez

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53

Huggingface

readability-es-sentences

hackathon-pln-es

Platform did not provide a description for this model.

$-/run

43

Huggingface

readability-es-3class-sentences