Text Classification
Transformers
ONNX
Safetensors
PEFT
Transformers.js
English
bert
lora
reproducible-research
synthetic-data
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ineeoluwa/adapterlab-support-intent-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ineeoluwa/adapterlab-support-intent-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ineeoluwa/adapterlab-support-intent-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ineeoluwa/adapterlab-support-intent-tiny") model = AutoModelForSequenceClassification.from_pretrained("Ineeoluwa/adapterlab-support-intent-tiny", device_map="auto") - PEFT
How to use Ineeoluwa/adapterlab-support-intent-tiny with PEFT:
Task type is invalid.
- Transformers.js
How to use Ineeoluwa/adapterlab-support-intent-tiny with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'Ineeoluwa/adapterlab-support-intent-tiny'); - Notebooks
- Google Colab
- Kaggle
File size: 1,902 Bytes
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"status": "passed",
"test_samples": 30,
"original_vs_merged_max_absolute_logit_error": 4.76837158203125e-07,
"merged_vs_onnx_max_absolute_logit_error": 6.556510925292969e-07,
"prediction_agreement": 1.0,
"onnx_metrics": {
"accuracy": 0.5666666666666667,
"macro_f1": 0.51994301994302,
"samples": 30,
"labels": [
"account",
"billing",
"cancellation",
"delivery",
"login",
"technical"
],
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"per_class": [
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"label": "billing",
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"precision": 0.375,
"recall": 0.6,
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{
"label": "login",
"precision": 1.0,
"recall": 0.8,
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"support": 5
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{
"label": "technical",
"precision": 0.625,
"recall": 1.0,
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"support": 5
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},
"additional_dynamic_shape_batches": 3
}
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