Text Classification
Transformers
ONNX
Safetensors
mojev-scorer
feature-extraction
calibration
structured-output
multiple-choice
preference-learning
multimodal
mojev
custom_code
Eval Results (legacy)
Instructions to use MoLeMo-Lab/mojev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoLeMo-Lab/mojev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MoLeMo-Lab/mojev", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MoLeMo-Lab/mojev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "source_revision": "d439315bd9a11409584e16758bb76a9d75b5bea7", | |
| "source_encoder_dtype": "torch.bfloat16", | |
| "scope": "Four English/Chinese text cases for numerical comparison.", | |
| "cases": [ | |
| { | |
| "context": "The customer was charged twice for the same order and wants the duplicate payment returned.", | |
| "question": "Which team should handle this request?", | |
| "candidates": [ | |
| "Billing", | |
| "Technical support", | |
| "Sales" | |
| ], | |
| "tokens": 40, | |
| "reference": [ | |
| 0.9212515354156494, | |
| 0.04107579588890076, | |
| 0.037672750651836395 | |
| ], | |
| "quantized": [ | |
| 0.8891163468360901, | |
| 0.05538984015583992, | |
| 0.05549393594264984 | |
| ], | |
| "quantized_logits": [ | |
| 2.8020951747894287, | |
| 0.0262632817029953, | |
| 0.028140777722001076 | |
| ], | |
| "top1_agrees": true, | |
| "max_probability_error": 0.032135188579559326, | |
| "onnx_cpu_seconds": 0.33495458390098065 | |
| }, | |
| { | |
| "context": "The red box is empty. The blue box contains the key.", | |
| "question": "Which box contains the key?", | |
| "candidates": [ | |
| "The red box", | |
| "The blue box" | |
| ], | |
| "tokens": 39, | |
| "reference": [ | |
| 0.020697807893157005, | |
| 0.9793022274971008 | |
| ], | |
| "quantized": [ | |
| 0.03171052783727646, | |
| 0.9682894349098206 | |
| ], | |
| "quantized_logits": [ | |
| 0.6149787306785583, | |
| 4.03386116027832 | |
| ], | |
| "top1_agrees": true, | |
| "max_probability_error": 0.011012792587280273, | |
| "onnx_cpu_seconds": 0.2776173329912126 | |
| }, | |
| { | |
| "context": "A parcel must arrive by Friday. Express delivery arrives Thursday; standard delivery arrives Monday.", | |
| "question": "Which delivery option meets the deadline?", | |
| "candidates": [ | |
| "Express delivery", | |
| "Standard delivery" | |
| ], | |
| "tokens": 40, | |
| "reference": [ | |
| 0.30602750182151794, | |
| 0.6939724683761597 | |
| ], | |
| "quantized": [ | |
| 0.26124119758605957, | |
| 0.7387588024139404 | |
| ], | |
| "quantized_logits": [ | |
| 2.2718594074249268, | |
| 3.311386823654175 | |
| ], | |
| "top1_agrees": true, | |
| "max_probability_error": 0.04478633403778076, | |
| "onnx_cpu_seconds": 0.2724832500098273 | |
| }, | |
| { | |
| "context": "用户说:订单被重复扣款,希望退回多付的钱。", | |
| "question": "应该把请求转给哪个团队?", | |
| "candidates": [ | |
| "账单与退款", | |
| "技术支持", | |
| "销售咨询" | |
| ], | |
| "tokens": 47, | |
| "reference": [ | |
| 0.9347994327545166, | |
| 0.04668309539556503, | |
| 0.018517442047595978 | |
| ], | |
| "quantized": [ | |
| 0.8895391821861267, | |
| 0.08191443234682083, | |
| 0.028546439483761787 | |
| ], | |
| "quantized_logits": [ | |
| 2.27278733253479, | |
| -0.11224094033241272, | |
| -1.166383981704712 | |
| ], | |
| "top1_agrees": true, | |
| "max_probability_error": 0.04526025056838989, | |
| "onnx_cpu_seconds": 0.360311041935347 | |
| } | |
| ] | |
| } | |