File size: 2,984 Bytes
9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf 9710d8f ff6f1bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
library_name: transformers
license: cc-by-4.0
base_model: allegro/herbert-large-cased
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: herbert-large-cased-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies
type: universal_dependencies
config: pl_pdb
split: validation
args: pl_pdb
metrics:
- name: Precision
type: precision
value: 0.91656329817706
- name: Recall
type: recall
value: 0.8825519391481612
- name: F1
type: f1
value: 0.892780213659273
- name: Accuracy
type: accuracy
value: 0.9827837758972863
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# herbert-large-cased-upos
This model is a fine-tuned version of [allegro/herbert-large-cased](https://huggingface.co/allegro/herbert-large-cased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9166
- Recall: 0.8826
- F1: 0.8928
- Accuracy: 0.9828
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 438 | 0.2798 | 0.8362 | 0.8222 | 0.8271 | 0.8779 |
| No log | 2.0 | 876 | 0.1613 | 0.9287 | 0.8511 | 0.8677 | 0.9240 |
| No log | 3.0 | 1314 | 0.0967 | 0.8845 | 0.8530 | 0.8562 | 0.9539 |
| No log | 4.0 | 1752 | 0.0917 | 0.9103 | 0.8461 | 0.8657 | 0.9629 |
| No log | 5.0 | 2190 | 0.0782 | 0.8965 | 0.8704 | 0.8764 | 0.9666 |
| No log | 6.0 | 2628 | 0.0766 | 0.8973 | 0.8704 | 0.8767 | 0.9691 |
| No log | 7.0 | 3066 | 0.0634 | 0.9171 | 0.8811 | 0.8923 | 0.9790 |
| No log | 8.0 | 3504 | 0.0626 | 0.9139 | 0.8909 | 0.8989 | 0.9796 |
| No log | 9.0 | 3942 | 0.0675 | 0.9131 | 0.8792 | 0.8893 | 0.9803 |
| No log | 10.0 | 4380 | 0.0611 | 0.9166 | 0.8826 | 0.8928 | 0.9828 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|