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
File size: 3,046 Bytes
0c8695b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | {
"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
}
]
}
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