1 ---
2 tags:
3 - token-classification
4 - bert
5 - adapterhub:ner/mit_movie_trivia
6 - adapter-transformers
7 language:
8 - en
9 ---
10
11 # Adapter `AdapterHub/bert-base-uncased-pf-mit_movie_trivia` for bert-base-uncased
12
13 An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging.
14
15 This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
16
17 ## Usage
18
19 First, install `adapter-transformers`:
20
21 ```
22 pip install -U adapter-transformers
23 ```
24 _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
25
26 Now, the adapter can be loaded and activated like this:
27
28 ```python
29 from transformers import AutoModelWithHeads
30
31 model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
32 adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mit_movie_trivia", source="hf")
33 model.active_adapters = adapter_name
34 ```
35
36 ## Architecture & Training
37
38 The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
39 In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
40
41
42 ## Evaluation results
43
44 Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
45
46 ## Citation
47
48 If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
49
50 ```bibtex
51 @inproceedings{poth-etal-2021-pre,
52 title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
53 author = {Poth, Clifton and
54 Pfeiffer, Jonas and
55 R{"u}ckl{'e}, Andreas and
56 Gurevych, Iryna},
57 booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
58 month = nov,
59 year = "2021",
60 address = "Online and Punta Cana, Dominican Republic",
61 publisher = "Association for Computational Linguistics",
62 url = "https://aclanthology.org/2021.emnlp-main.827",
63 pages = "10585--10605",
64 }
65 ```