File size: 2,396 Bytes
647ca25
fdb74d2
 
 
 
647ca25
fdb74d2
dab1798
21a75b2
 
647ca25
02a4bad
b28f7ec
 
 
 
02a4bad
b28f7ec
 
02a4bad
b28f7ec
 
02a4bad
b28f7ec
02a4bad
b28f7ec
 
 
 
 
 
 
 
 
 
 
 
f6fc929
b28f7ec
 
 
 
 
 
 
 
 
 
 
 
 
02a4bad
b28f7ec
 
 
 
02a4bad
b28f7ec
 
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
---
datasets: 
  - wiki-corpus
language: 
  - lt
license: apache-2.0
tags: 
  - "text-generation"
widget:
  - text: "Lietuvos yra viena "
---
## Model description
GPT-2 model from Lithuania using Wikipedia corpus dataset based on GPT-2 small model.

This is only the first version of the model, over time model will be improved using a bigger dataset and better data preparation.

## Training data
This model was pre-trained with 180MB of Lithuanian Wikipedia. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE).

## Training
The model was trained on wiki-corpus for 40 hours using NVIDIA Tesla P100 GPU.

## How to use

### Load model

``` from transformers import AutoTokenizer, TFAutoModelWithLMHead
import tensorflow as tf

tokenizer = AutoTokenizer.from_pretrained("DeividasM/gpt2_lithuanian_small")
model = TFAutoModelWithLMHead.from_pretrained("DeividasM/gpt2_lithuanian_small")

# Get sequence length max of 1024
tokenizer.model_max_length=1024 

model.eval()
```
## Generate text

``` text = "tekstas"
inputs = tokenizer.encode(text, return_tensors="tf")


outputs = model.generate(inputs, eos_token_id=50256, pad_token_id=50256, 
                         do_sample=True,
                         max_length=40,
                         top_k=40)
                         
print(tokenizer.decode(outputs[0]))

```
## Limitations and bias
The training data used for this model come from Lithuanian Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card:

"Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes."

## Author

Lithuanian GPT-2 small was trained and evaluated by Deividas Mataciunas