cocorooxinnn/finetuned_bart_mnli_eu_taxonomie

Model Card for Model ID

This is a facebook/bart-large-mnli model with a binary classiication head finetuned using PEFT LoRA on a dataset created from EU taxonomie.

Model Details

Model Description

This is a facebook/bart-large-mnli model finetuned on a dataset created from EU taxonomie. The model has a single neuron output for binary classification as to predict whether a keyword is semantically contained/relevant within a given topic. The topics can be from any domain.

Tasks	Positive example	Negative example
Topic	Fashion industry	Healthcare
Keyword	Pollution of textile production	Football athlete
Prediction	Relevant	Irrelevant

• Developed by: [Xin Chen]
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• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: finetuned from facebook/bart-large-mnli
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Uses

Topic keyword denoising for topic modeling.

Recommendations

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import BartConfig, BartForSequenceClassification, BartTokenizerFast
config = BartConfig(num_labels = 1)
tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-large-mnli")
model = BartForSequenceClassification.from_pretrained("cocorooxinnn/finetuned_bart_mnli_eu_taxonomie", 
device_map = "auto", 
config = config)
m = nn.Sigmoid
#Simple NLI template for premise, hypothesis
template = "They are talking about "
premise, hypothesis = (template + "Fashion industry", template + "Pollution of textile production ")
x = tokenizer(premise, hypothesis,return_tensors='pt').to(ort_model.device)
prediction = model(model(**x).logits).squeeze() > 0.5  # batch_size, 1

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Training Details

Training Data

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-4
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• num_epochs: 2

Training results (F-score is calculated on validation set)

Step	Training Loss	Validation Loss	F1	Precision	Recall	Accuracy
50	0.441800	0.285273	0.882523	0.892977	0.872311	0.883930
100	0.328400	0.259236	0.892380	0.903460	0.881568	0.893727
150	0.287400	0.257763	0.908518	0.877091	0.942282	0.905157
200	0.286300	0.243944	0.909042	0.898643	0.919684	0.908015
250	0.275500	0.239769	0.894515	0.925225	0.865777	0.897945
300	0.271800	0.222483	0.912901	0.912653	0.913150	0.912913
350	0.245500	0.221774	0.916039	0.905560	0.926763	0.915090
400	0.250700	0.219120	0.912324	0.924044	0.900898	0.913458
450	0.249000	0.211039	0.914482	0.922204	0.906888	0.915227
500	0.241500	0.207655	0.927005	0.910691	0.943915	0.925704
550	0.249900	0.197901	0.924230	0.925239	0.923224	0.924343
600	0.236000	0.202164	0.921937	0.929204	0.914784	0.922574
650	0.196100	0.192816	0.931687	0.918740	0.945004	0.930739
700	0.214800	0.206045	0.930494	0.894499	0.969507	0.927609
750	0.223600	0.186433	0.931180	0.928533	0.933842	0.931011
800	0.223900	0.189542	0.933564	0.911757	0.956439	0.931964
850	0.197500	0.191664	0.930473	0.928204	0.932753	0.930331
900	0.194600	0.185483	0.930797	0.922460	0.939287	0.930195
950	0.190200	0.183808	0.934791	0.916100	0.954261	0.933460
1000	0.189700	0.181666	0.934212	0.923404	0.945276	0.933460
1050	0.199300	0.177857	0.933693	0.924473	0.943098	0.933052

TrainOutput(global_step=1072, training_loss=0.2457642003671447, metrics={'train_runtime': 3750.3603, 'train_samples_per_second': 18.289, 'train_steps_per_second': 0.286, 'total_flos': 7425156147297000.0, 'train_loss': 0.2457642003671447, 'epoch': 2.0})

Evaluation on testset

Precision	Recall	F-score
0.94	0.94	0.94

Framework versions

• PEFT 0.10.0
• Transformers 4.41.2
• Pytorch 2.2.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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