Finetuning `wav2vec2-large-xlsr-53` on Thai Common Voice 7.0 We finetune wav2vec2-large-xlsr-53 based on Fine-tuning Wav2Vec2 for English ASR using Thai examples of Common Voice Corpus 7.0. The notebooks and scripts can be found in vistec-ai/wav2vec2-large-xlsr-53-th. The pretrained model and processor can be found at airesearch/wav2vec2-large-xlsr-53-th. Add `syllable_tokenize`, `word_tokenize` (PyThaiNLP) and deepcut tokenizers to `eval.py` from robust-speech-event
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View details ↗Differences between GENA-LM (`gena-lm-bigbird-base-t2t`) and DNABERT: - BPE tokenization instead of k-mers; - input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT; - pre-training on T2T vs. GRCh38.p13 human genome assembly. This repository also contains models that are finetuned on downstream tasks and models that are used in our GENA-Web web tool for genomic sequence annotation: - splice sites prediction (branch gena_web_spliceai) GENA-LM (`gena-lm-bigbird-base-t2t`) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for `gena-lm-bigbird-base-t2t` is similar to the `google/bigbird-roberta-base`:
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View details ↗DPLM (diffusion protein language model) is a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. Specifically, DPLM exhibits impressive performance in protein sequence generation, motif scaffolding, inverse folding, and representation learning. For more detailed information about DPLM, please refer to our paper Diffusion Language Models Are Versatile Protein Learners. This repository contains the DPLM model checkpoint of 150M parameters. Please refer to our github repository for code and usage. For example, you can load DPLM model as below: All DPLM checkpoints are available in the table below: | Model size | Num layers | Num parameters | |------------------------------|----|----------| | dplm_3b | 36 | 3B | | dplm_650m | 33 | 650M | | dplm_150m | 30 | 150M |
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View details ↗DPLM (diffusion protein language model) is a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. Specifically, DPLM exhibits impressive performance in protein sequence generation, motif scaffolding, inverse folding, and representation learning. For more detailed information about DPLM, please refer to our paper Diffusion Language Models Are Versatile Protein Learners. This repository contains the DPLM model checkpoint of 650M parameters. Please refer to our github repository for code and usage. For example, you can load DPLM model as below: All DPLM checkpoints are available in the table below: | Model size | Num layers | Num parameters | |------------------------------|----|----------| | dplm_3b | 36 | 3B | | dplm_650m | 33 | 650M | | dplm_150m | 30 | 150M |
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View details ↗- **Developed by:** Products Pillar, AI Singapore - **Funded by:** Singapore NRF - **Model type:** Decoder - **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese - **License:** Gemma Community License We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our continued pre-trained Gemma-SEA-LION-v3-9B, a decoder model using the Gemma2 architecture, to create Gemma-SEA-LION-v3-9B-IT. For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
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