Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use elopezlopez/distilbert-base-uncased_fold_4_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/distilbert-base-uncased_fold_4_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/distilbert-base-uncased_fold_4_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/distilbert-base-uncased_fold_4_binary") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/distilbert-base-uncased_fold_4_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
5b0c3fe
1
Parent(s): d2cf9ea
update model card README.md
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- f1
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model-index:
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- name: distilbert-base-uncased_fold_4_binary
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilbert-base-uncased_fold_4_binary
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2977
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- F1: 0.8083
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 25
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| No log | 1.0 | 289 | 0.3701 | 0.7903 |
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| 0.4005 | 2.0 | 578 | 0.3669 | 0.7994 |
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| 0.4005 | 3.0 | 867 | 0.5038 | 0.7955 |
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| 0.1945 | 4.0 | 1156 | 0.6353 | 0.8006 |
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| 0.1945 | 5.0 | 1445 | 0.8974 | 0.7826 |
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| 0.0909 | 6.0 | 1734 | 0.8533 | 0.7764 |
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| 0.0389 | 7.0 | 2023 | 0.9969 | 0.7957 |
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| 0.0389 | 8.0 | 2312 | 1.0356 | 0.7952 |
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| 0.0231 | 9.0 | 2601 | 1.1538 | 0.7963 |
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| 0.0231 | 10.0 | 2890 | 1.2011 | 0.7968 |
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| 0.0051 | 11.0 | 3179 | 1.2329 | 0.7935 |
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| 0.0051 | 12.0 | 3468 | 1.2829 | 0.8056 |
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| 0.0066 | 13.0 | 3757 | 1.2946 | 0.7956 |
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| 0.004 | 14.0 | 4046 | 1.2977 | 0.8083 |
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| 0.004 | 15.0 | 4335 | 1.3970 | 0.7957 |
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| 0.0007 | 16.0 | 4624 | 1.3361 | 0.7917 |
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| 0.0007 | 17.0 | 4913 | 1.5782 | 0.7954 |
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| 0.0107 | 18.0 | 5202 | 1.4641 | 0.7900 |
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| 0.0107 | 19.0 | 5491 | 1.4490 | 0.7957 |
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| 0.0058 | 20.0 | 5780 | 1.4607 | 0.7932 |
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| 0.0016 | 21.0 | 6069 | 1.5048 | 0.7939 |
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| 0.0016 | 22.0 | 6358 | 1.5219 | 0.7945 |
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| 0.0027 | 23.0 | 6647 | 1.4783 | 0.7937 |
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| 0.0027 | 24.0 | 6936 | 1.4715 | 0.7981 |
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| 0.0004 | 25.0 | 7225 | 1.4989 | 0.7900 |
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### Framework versions
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- Transformers 4.21.0
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- Pytorch 1.12.0+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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