classifier_v5
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3360
- Accuracy: 0.9548
- F1: 0.9548
- Precision: 0.9548
- Recall: 0.9548
Model description
The model is used to classify text of the interactions for users to decided if they belong to:
- education
- general
- maths
- news
- restaurants
- weather
Training and evaluation data
The data used is the dataset built on:
- MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
- And added some interactions from our own app to it.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.9999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 800
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.7727 | 1.0 | 856 | 0.3245 | 0.9338 | 0.9338 | 0.9338 | 0.9338 |
0.2747 | 2.0 | 1712 | 0.2847 | 0.9413 | 0.9413 | 0.9413 | 0.9413 |
0.1488 | 3.0 | 2568 | 0.3170 | 0.9473 | 0.9473 | 0.9473 | 0.9473 |
0.0911 | 4.0 | 3424 | 0.3333 | 0.9488 | 0.9488 | 0.9488 | 0.9488 |
0.0642 | 5.0 | 4280 | 0.3360 | 0.9549 | 0.9549 | 0.9549 | 0.9549 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
Evaluation results
On test dataset:
precision recall f1-score support
education 0.99 0.87 0.93 189
general 0.91 0.95 0.93 594
maths 1.00 0.95 0.97 75
news 0.93 0.95 0.94 372
restaurants 0.97 0.99 0.98 165
weather 0.99 0.96 0.97 468
accuracy 0.95 1863
macro avg 0.97 0.95 0.95 1863
weighted avg 0.95 0.95 0.95 1863
Tagged dataset from direct responses on the app:
precision recall f1-score support
education 0.48 0.55 0.51 229
general 0.80 0.79 0.79 1367
maths 0.96 0.76 0.85 231
news 0.45 0.60 0.51 188
restaurants 0.86 0.95 0.90 158
weather 0.91 0.65 0.76 186
accuracy 0.75 2359
macro avg 0.74 0.72 0.72 2359
weighted avg 0.77 0.75 0.75 2359
Demo: How to use in HuggingFace Transformers Pipeline
Requires transformers: pip install transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
model_name = 'SoyLuzia/classifier_v5'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
res = classifier("¿Qué tiempo hace hoy en Villena?")
print(res)
Outputs:
[{'label': 'weather', 'score': 0.9999312162399292}]
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