Fine-tuned facebook/wav2vec2-large-xlsr-53 for Kazakh ASR using the Kazakh Speech Corpus v1.1 The model can be evaluated as follows on the test set of Kazakh Speech Corpus v1.1. To evaluate, download the archive, untar and pass the path to data to `get_test_dataset` as below:

Speech recognition

Input Audio→ Output Text

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Llama-3.1-8B-bnb-4bit-wenyanwen

AISPIN

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Category pending

Input Not verified→ Output Not verified

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bolt-instruct-v2

aisquared

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Visual QA

Input Text · Image→ Output Text

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bolt-instruct-v2-gguf

aisquared

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Visual QA

Input Text · Image→ Output Text

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telecom-intelligence-model-v7-merged

Aitana47

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Category pending

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Vi-Qwen2-3B-RAG

AITeamVN

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

Input Text→ Output Text

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Vietnamese_Embedding

AITeamVN

Vietnamese_Embedding is an embedding model fine-tuned from the BGE-M3 model (https://huggingface.co/BAAI/bge-m3) to enhance retrieval capabilities for Vietnamese. Although the score on the legal domain drops a bit on Vietnamese_Embedding_v2, since this phase data is much larger, it is very good for other domains. You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).

Embeddings & similarity

Input Text→ Output Embedding

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Vietnamese_Embedding_v2

AITeamVN

Vietnamese_Embedding_v2 is an embedding model fine-tuned from the BGE-M3 model (https://huggingface.co/BAAI/bge-reranker-v2-m3) to enhance retrieval capabilities for Vietnamese. Although the score on the legal domain drops a bit on Vietnamese_Embedding (Phase 2), since this phase data is much larger, it is good for other domains. You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).

Embeddings & similarity

Input Text→ Output Embedding

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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