Instructions to use vppvgit/Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vppvgit/Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vppvgit/Finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vppvgit/Finetuned") model = AutoModelForMaskedLM.from_pretrained("vppvgit/Finetuned") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- generated_from_trainer
|
| 4 |
+
datasets:
|
| 5 |
+
- null
|
| 6 |
+
model-index:
|
| 7 |
+
- name: BiblItBERT-1
|
| 8 |
+
results:
|
| 9 |
+
- task:
|
| 10 |
+
name: Masked Language Modeling
|
| 11 |
+
type: fill-mask
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 15 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 16 |
+
|
| 17 |
+
# BiblItBERT-1
|
| 18 |
+
|
| 19 |
+
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset.
|
| 20 |
+
It achieves the following results on the evaluation set:
|
| 21 |
+
- Loss: 0.7784
|
| 22 |
+
|
| 23 |
+
## Model description
|
| 24 |
+
|
| 25 |
+
More information needed
|
| 26 |
+
|
| 27 |
+
## Intended uses & limitations
|
| 28 |
+
|
| 29 |
+
More information needed
|
| 30 |
+
|
| 31 |
+
## Training and evaluation data
|
| 32 |
+
|
| 33 |
+
More information needed
|
| 34 |
+
|
| 35 |
+
## Training procedure
|
| 36 |
+
|
| 37 |
+
### Training hyperparameters
|
| 38 |
+
|
| 39 |
+
The following hyperparameters were used during training:
|
| 40 |
+
- learning_rate: 2e-05
|
| 41 |
+
- train_batch_size: 8
|
| 42 |
+
- eval_batch_size: 8
|
| 43 |
+
- seed: 0
|
| 44 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 45 |
+
- lr_scheduler_type: linear
|
| 46 |
+
- num_epochs: 50
|
| 47 |
+
|
| 48 |
+
### Training results
|
| 49 |
+
|
| 50 |
+
| Training Loss | Epoch | Step | Validation Loss |
|
| 51 |
+
|:-------------:|:-----:|:------:|:---------------:|
|
| 52 |
+
| 1.5764 | 1.0 | 16528 | 1.5214 |
|
| 53 |
+
| 1.4572 | 2.0 | 33056 | 1.4201 |
|
| 54 |
+
| 1.3787 | 3.0 | 49584 | 1.3728 |
|
| 55 |
+
| 1.3451 | 4.0 | 66112 | 1.3245 |
|
| 56 |
+
| 1.3066 | 5.0 | 82640 | 1.2614 |
|
| 57 |
+
| 1.2447 | 6.0 | 99168 | 1.2333 |
|
| 58 |
+
| 1.2172 | 7.0 | 115696 | 1.2149 |
|
| 59 |
+
| 1.2079 | 8.0 | 132224 | 1.1853 |
|
| 60 |
+
| 1.2167 | 9.0 | 148752 | 1.1586 |
|
| 61 |
+
| 1.2056 | 10.0 | 165280 | 1.1503 |
|
| 62 |
+
| 1.1307 | 11.0 | 181808 | 1.1224 |
|
| 63 |
+
| 1.1689 | 12.0 | 198336 | 1.1074 |
|
| 64 |
+
| 1.1007 | 13.0 | 214864 | 1.0924 |
|
| 65 |
+
| 1.0901 | 14.0 | 231392 | 1.0659 |
|
| 66 |
+
| 1.0667 | 15.0 | 247920 | 1.0650 |
|
| 67 |
+
| 1.0434 | 16.0 | 264448 | 1.0362 |
|
| 68 |
+
| 1.0333 | 17.0 | 280976 | 1.0250 |
|
| 69 |
+
| 1.0342 | 18.0 | 297504 | 1.0198 |
|
| 70 |
+
| 1.0059 | 19.0 | 314032 | 0.9950 |
|
| 71 |
+
| 0.9719 | 20.0 | 330560 | 0.9836 |
|
| 72 |
+
| 0.9863 | 21.0 | 347088 | 0.9873 |
|
| 73 |
+
| 0.9781 | 22.0 | 363616 | 0.9724 |
|
| 74 |
+
| 0.9369 | 23.0 | 380144 | 0.9599 |
|
| 75 |
+
| 0.9578 | 24.0 | 396672 | 0.9557 |
|
| 76 |
+
| 0.9253 | 25.0 | 413200 | 0.9400 |
|
| 77 |
+
| 0.9441 | 26.0 | 429728 | 0.9222 |
|
| 78 |
+
| 0.9138 | 27.0 | 446256 | 0.9140 |
|
| 79 |
+
| 0.882 | 28.0 | 462784 | 0.9045 |
|
| 80 |
+
| 0.864 | 29.0 | 479312 | 0.8880 |
|
| 81 |
+
| 0.8632 | 30.0 | 495840 | 0.9023 |
|
| 82 |
+
| 0.8342 | 32.0 | 528896 | 0.8740 |
|
| 83 |
+
| 0.8037 | 34.0 | 561952 | 0.8647 |
|
| 84 |
+
| 0.8119 | 37.0 | 611536 | 0.8358 |
|
| 85 |
+
| 0.8011 | 38.0 | 628064 | 0.8252 |
|
| 86 |
+
| 0.786 | 39.0 | 644592 | 0.8228 |
|
| 87 |
+
| 0.7697 | 41.0 | 677648 | 0.8138 |
|
| 88 |
+
| 0.7485 | 42.0 | 694176 | 0.8104 |
|
| 89 |
+
| 0.7689 | 43.0 | 710704 | 0.8018 |
|
| 90 |
+
| 0.7401 | 45.0 | 743760 | 0.7957 |
|
| 91 |
+
| 0.7031 | 47.0 | 776816 | 0.7726 |
|
| 92 |
+
| 0.7578 | 48.0 | 793344 | 0.7864 |
|
| 93 |
+
| 0.7298 | 49.0 | 809872 | 0.7775 |
|
| 94 |
+
| 0.707 | 50.0 | 826400 | 0.7784 |
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
### Framework versions
|
| 98 |
+
|
| 99 |
+
- Transformers 4.10.3
|
| 100 |
+
- Pytorch 1.9.0+cu102
|
| 101 |
+
- Datasets 1.12.1
|
| 102 |
+
- Tokenizers 0.10.3
|