Instructions to use simonschoe/TransformationTransformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonschoe/TransformationTransformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simonschoe/TransformationTransformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simonschoe/TransformationTransformer") model = AutoModelForSequenceClassification.from_pretrained("simonschoe/TransformationTransformer") - Notebooks
- Google Colab
- Kaggle
Commit ·
056293e
1
Parent(s): 9af577a
add tokenizer
Browse files- tokenizer.json +4 -2
tokenizer.json
CHANGED
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@@ -53,7 +53,8 @@
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"pre_tokenizer": {
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"type": "ByteLevel",
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"add_prefix_space": false,
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"trim_offsets": true
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},
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"post_processor": {
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"type": "RobertaProcessing",
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"decoder": {
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"type": "ByteLevel",
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"add_prefix_space": true,
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"trim_offsets": true
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},
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"model": {
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"type": "BPE",
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"pre_tokenizer": {
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"type": "ByteLevel",
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"add_prefix_space": false,
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"trim_offsets": true,
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"use_regex": true
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},
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"post_processor": {
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"type": "RobertaProcessing",
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"decoder": {
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"type": "ByteLevel",
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"add_prefix_space": true,
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"trim_offsets": true,
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"use_regex": true
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},
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"model": {
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"type": "BPE",
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