Instructions to use yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
CHANGED
|
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 20 |
|
| 21 |
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
-
- Loss: 0.
|
| 24 |
-
- Precision: 0.
|
| 25 |
-
- Recall: 0.
|
| 26 |
-
- F1: 0.
|
| 27 |
-
- Accuracy: 0.
|
| 28 |
|
| 29 |
## Model description
|
| 30 |
|
|
@@ -49,32 +49,18 @@ The following hyperparameters were used during training:
|
|
| 49 |
- seed: 42
|
| 50 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 51 |
- lr_scheduler_type: linear
|
| 52 |
-
- num_epochs:
|
| 53 |
|
| 54 |
### Training results
|
| 55 |
|
| 56 |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 57 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 58 |
-
| 0.
|
| 59 |
-
| 0.0125 | 2.0 | 1198 | 0.0184 | 0.4039 | 0.2770 | 0.3287 | 0.9958 |
|
| 60 |
-
| 0.0099 | 3.0 | 1797 | 0.0141 | 0.4528 | 0.4865 | 0.4691 | 0.9958 |
|
| 61 |
-
| 0.0082 | 4.0 | 2396 | 0.0279 | 0.4558 | 0.5743 | 0.5082 | 0.9958 |
|
| 62 |
-
| 0.0074 | 5.0 | 2995 | 0.0138 | 0.5153 | 0.6824 | 0.5872 | 0.9960 |
|
| 63 |
-
| 0.0034 | 6.0 | 3594 | 0.0193 | 0.4934 | 0.6284 | 0.5527 | 0.9953 |
|
| 64 |
-
| 0.0046 | 7.0 | 4193 | 0.0190 | 0.5305 | 0.7635 | 0.6260 | 0.9960 |
|
| 65 |
-
| 0.0024 | 8.0 | 4792 | 0.0286 | 0.5670 | 0.6858 | 0.6208 | 0.9955 |
|
| 66 |
-
| 0.0024 | 9.0 | 5391 | 0.0270 | 0.5889 | 0.5709 | 0.5798 | 0.9953 |
|
| 67 |
-
| 0.0034 | 10.0 | 5990 | 0.0339 | 0.5623 | 0.6858 | 0.6180 | 0.9953 |
|
| 68 |
-
| 0.0015 | 11.0 | 6589 | 0.0373 | 0.6122 | 0.7095 | 0.6573 | 0.9947 |
|
| 69 |
-
| 0.001 | 12.0 | 7188 | 0.0361 | 0.5519 | 0.6284 | 0.5877 | 0.9950 |
|
| 70 |
-
| 0.0005 | 13.0 | 7787 | 0.0353 | 0.5658 | 0.6824 | 0.6187 | 0.9950 |
|
| 71 |
-
| 0.0007 | 14.0 | 8386 | 0.0355 | 0.5556 | 0.7264 | 0.6296 | 0.9948 |
|
| 72 |
-
| 0.0003 | 15.0 | 8985 | 0.0360 | 0.5404 | 0.7230 | 0.6185 | 0.9949 |
|
| 73 |
|
| 74 |
|
| 75 |
### Framework versions
|
| 76 |
|
| 77 |
-
- Transformers 4.
|
| 78 |
-
- Pytorch 2.
|
| 79 |
-
- Datasets 2.
|
| 80 |
-
- Tokenizers 0.
|
|
|
|
| 20 |
|
| 21 |
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
|
| 22 |
It achieves the following results on the evaluation set:
|
| 23 |
+
- Loss: 0.0008
|
| 24 |
+
- Precision: 0.6804
|
| 25 |
+
- Recall: 0.9301
|
| 26 |
+
- F1: 0.7859
|
| 27 |
+
- Accuracy: 0.9997
|
| 28 |
|
| 29 |
## Model description
|
| 30 |
|
|
|
|
| 49 |
- seed: 42
|
| 50 |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 51 |
- lr_scheduler_type: linear
|
| 52 |
+
- num_epochs: 1
|
| 53 |
|
| 54 |
### Training results
|
| 55 |
|
| 56 |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 57 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 58 |
+
| 0.0009 | 1.0 | 3534 | 0.0008 | 0.6804 | 0.9301 | 0.7859 | 0.9997 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 59 |
|
| 60 |
|
| 61 |
### Framework versions
|
| 62 |
|
| 63 |
+
- Transformers 4.40.1
|
| 64 |
+
- Pytorch 2.3.0+cu121
|
| 65 |
+
- Datasets 2.19.0
|
| 66 |
+
- Tokenizers 0.19.1
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 433368892
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be962aeffe91ea96823f146ed8b020c03ea8f542798497b6cc5bb67bfd119ba8
|
| 3 |
size 433368892
|
runs/May01_23-42-22_e0b25f996e6d/events.out.tfevents.1714606956.e0b25f996e6d.661.1
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c679fe786e2bb5c0b1de719b1e9bb1f776d98fdc0beee24d03301ffa36e7a8ad
|
| 3 |
+
size 7521
|
runs/May01_23-42-22_e0b25f996e6d/events.out.tfevents.1714612002.e0b25f996e6d.661.2
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:eeec49e5197c895763be4f02e3a59b1466975fa9ff2664c97501ea626032359a
|
| 3 |
+
size 560
|