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AI 모델 84760
- Ornith-1.5-35B-A3B-M3TiS-3090↗
CanadaApollo6
- ctxl-rerank-v2-instruct-multilingual-2b↗
ContextualAI
- banglabert↗
csebuetnlp
This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an ELECTRA discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali. For finetuning on different downstream tasks such as `Sentiment classification`, `Named Entity Recognition`, `Natural Language Inference` etc., refer to the scripts in the official GitHub repository. **Note**: This model was pretrained using a specific normalization pipeline available here. All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:
- DanbotNL-2408-260M↗
dartags
- Qwen3.5-9B-GGUF↗
duolaf
- gpt-neo-1.3B↗
EleutherAI
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model. GPT-Neo 1.3B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. This model was trained on the Pile for 380 billion tokens over 362,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
- BigBang-v1↗
endless-frontier
- bart-large-cnn↗
facebook
BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
- mms-tts-ata↗
facebook
- mms-tts-nim↗
facebook