File size: 2,923 Bytes
f623cc1 |
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 |
---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-cased-upos
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies
type: universal_dependencies
config: en_ewt
split: validation
args: en_ewt
metrics:
- name: Precision
type: precision
value: 0.8688031595250056
- name: Recall
type: recall
value: 0.8557292884764056
- name: F1
type: f1
value: 0.8617720995316154
- name: Accuracy
type: accuracy
value: 0.8904395106479384
---
<!-- 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. -->
# bert-large-cased-upos
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4114
- Precision: 0.8688
- Recall: 0.8557
- F1: 0.8618
- Accuracy: 0.8904
## 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 | 0.5774 | 0.8348 | 0.7595 | 0.7893 | 0.8099 |
| No log | 2.0 | 876 | 0.4787 | 0.8114 | 0.7967 | 0.8000 | 0.8385 |
| No log | 3.0 | 1314 | 0.4345 | 0.8227 | 0.8302 | 0.8213 | 0.8601 |
| No log | 4.0 | 1752 | 0.4140 | 0.8257 | 0.8430 | 0.8304 | 0.8727 |
| No log | 5.0 | 2190 | 0.4211 | 0.8405 | 0.8525 | 0.8441 | 0.8787 |
| No log | 6.0 | 2628 | 0.4114 | 0.8688 | 0.8557 | 0.8618 | 0.8904 |
| No log | 7.0 | 3066 | 0.4582 | 0.8454 | 0.8572 | 0.8503 | 0.8911 |
| No log | 8.0 | 3504 | 0.4771 | 0.8447 | 0.8588 | 0.8508 | 0.8894 |
| No log | 9.0 | 3942 | 0.4799 | 0.8545 | 0.8626 | 0.8577 | 0.8918 |
| No log | 10.0 | 4380 | 0.4919 | 0.8539 | 0.8642 | 0.8579 | 0.8937 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|