File size: 1,964 Bytes
452ee32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: scibert_scivocab_uncased-finetuned-mol-mlm-0.3
  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_scivocab_uncased-finetuned-mol-mlm-0.3

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5049

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8725        | 1.0   | 180  | 0.6730          |
| 0.6668        | 2.0   | 360  | 0.6203          |
| 0.6078        | 3.0   | 540  | 0.5739          |
| 0.5656        | 4.0   | 720  | 0.5537          |
| 0.5385        | 5.0   | 900  | 0.5483          |
| 0.5163        | 6.0   | 1080 | 0.5335          |
| 0.5043        | 7.0   | 1260 | 0.5350          |
| 0.4927        | 8.0   | 1440 | 0.5173          |
| 0.4841        | 9.0   | 1620 | 0.5093          |
| 0.4765        | 10.0  | 1800 | 0.5058          |
| 0.4709        | 11.0  | 1980 | 0.5104          |
| 0.4673        | 12.0  | 2160 | 0.5017          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0