File size: 2,541 Bytes
02b6b8c |
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 |
---
base_model: manucos/finetuned__beto-clinical-wl-es__augmented-ultrasounds
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned__beto-clinical-wl-es__augmented-ultrasounds-ner
results: []
---
<!-- 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. -->
# finetuned__beto-clinical-wl-es__augmented-ultrasounds-ner
This model is a fine-tuned version of [manucos/finetuned__beto-clinical-wl-es__augmented-ultrasounds](https://huggingface.co/manucos/finetuned__beto-clinical-wl-es__augmented-ultrasounds) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3556
- Precision: 0.8057
- Recall: 0.8897
- F1: 0.8456
- Accuracy: 0.9216
## 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: 2e-05
- train_batch_size: 8
- 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 | 206 | 0.2470 | 0.7303 | 0.8441 | 0.7831 | 0.9080 |
| No log | 2.0 | 412 | 0.2471 | 0.7632 | 0.8644 | 0.8106 | 0.9182 |
| 0.3075 | 3.0 | 618 | 0.2838 | 0.7661 | 0.8553 | 0.8082 | 0.9106 |
| 0.3075 | 4.0 | 824 | 0.3057 | 0.7802 | 0.8765 | 0.8255 | 0.9170 |
| 0.0622 | 5.0 | 1030 | 0.2932 | 0.7844 | 0.8836 | 0.8310 | 0.9204 |
| 0.0622 | 6.0 | 1236 | 0.3435 | 0.8037 | 0.8907 | 0.8449 | 0.9211 |
| 0.0622 | 7.0 | 1442 | 0.3464 | 0.8013 | 0.8856 | 0.8413 | 0.9196 |
| 0.0255 | 8.0 | 1648 | 0.3470 | 0.7980 | 0.8877 | 0.8404 | 0.9224 |
| 0.0255 | 9.0 | 1854 | 0.3556 | 0.8055 | 0.8887 | 0.8450 | 0.9201 |
| 0.0159 | 10.0 | 2060 | 0.3556 | 0.8057 | 0.8897 | 0.8456 | 0.9216 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|