File size: 2,376 Bytes
36a79f2 b52010d 36a79f2 b52010d 36a79f2 |
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: allenai/scibert_scivocab_cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scibert-finetuned-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. -->
# scibert-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3459
- Precision: 0.5666
- Recall: 0.5191
- F1: 0.5418
- Accuracy: 0.9363
## 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 | 121 | 0.3648 | 0.3157 | 0.3390 | 0.3269 | 0.8945 |
| No log | 2.0 | 242 | 0.3177 | 0.5280 | 0.3348 | 0.4097 | 0.9253 |
| No log | 3.0 | 363 | 0.2599 | 0.5143 | 0.4326 | 0.4700 | 0.9315 |
| No log | 4.0 | 484 | 0.2825 | 0.5360 | 0.4227 | 0.4726 | 0.9336 |
| 0.2574 | 5.0 | 605 | 0.2968 | 0.5473 | 0.4922 | 0.5183 | 0.9350 |
| 0.2574 | 6.0 | 726 | 0.3193 | 0.5857 | 0.4894 | 0.5332 | 0.9377 |
| 0.2574 | 7.0 | 847 | 0.3327 | 0.5513 | 0.4879 | 0.5177 | 0.9356 |
| 0.2574 | 8.0 | 968 | 0.3315 | 0.5658 | 0.5121 | 0.5376 | 0.9363 |
| 0.0678 | 9.0 | 1089 | 0.3413 | 0.5465 | 0.5163 | 0.5310 | 0.9361 |
| 0.0678 | 10.0 | 1210 | 0.3459 | 0.5666 | 0.5191 | 0.5418 | 0.9363 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|