m2m
Datasets
All datasets matching “m2m”m2mdatasetm2mcent-mcp-schemas
🌐 M2MCent Agentic Services - MCP Schemas Dataset
🚀 Empowering Autonomous AI on Base L2
This dataset contains the JSON schemas for 1,005 microservices natively available on the M2MCent Network via the x402 V2 Protocol (EIP-3009).
It is specifically designed for instruction-tuning LLMs (like Llama-3, Mistral, Qwen) so they can autonomously discover, negotiate, and consume monetized API endpoints using gasless cryptocurrency settlements on the Base L2 network.… See the full description on the dataset page: https://huggingface.co/datasets/evozim/m2mcent-mcp-schemas.m2m3_qualitative_analysis_ref_cmbert_io
m2m3_qualitative_analysis_ref_cmbert_io
Introduction
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
Approachrd : M2 and M3
Dataset type : ground-truth
Tokenizer : Jean-Baptiste/camembert-ner
Tagging format : IO
Counts :
Train : 6084
Dev : 676
Test : 1685
Associated… See the full description on the dataset page: https://huggingface.co/datasets/nlpso/m2m3_qualitative_analysis_ref_cmbert_io.rm-static-m2m100-zh-jiantim2m_smolvla_finetune_datasetThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "koch_follower",
"total_episodes": 20,
"total_frames": 15132,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/lomiotech/m2m_smolvla_finetune_dataset.m2m3_qualitative_analysis_ocr_ptrn_cmbert_io
m2m3_qualitative_analysis_ocr_ptrn_cmbert_io
Introduction
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
Approachrd : M2 and M3
Dataset type : noisy (Pero OCR)
Tokenizer : HueyNemud/das22-10-camembert_pretrained
Tagging format : IO
Counts :
Train : 6084
Dev : 676… See the full description on the dataset page: https://huggingface.co/datasets/nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_io.