Fill-Mask
Transformers
PyTorch
English
bart
text2text-generation
summarization
long context
custom_code
Instructions to use ccdv/lsg-bart-base-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccdv/lsg-bart-base-16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ccdv/lsg-bart-base-16384", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-16384", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
config
Browse files- config.json +1 -1
config.json
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@@ -58,7 +58,7 @@
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"normalize_before": false,
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"normalize_embedding": true,
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"num_beams": 5,
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-
"num_global_tokens":
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"num_hidden_layers": 6,
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"pad_token_id": 1,
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| 64 |
"pass_global_tokens_to_decoder": true,
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| 58 |
"normalize_before": false,
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"normalize_embedding": true,
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"num_beams": 5,
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+
"num_global_tokens": 16,
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"num_hidden_layers": 6,
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"pad_token_id": 1,
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| 64 |
"pass_global_tokens_to_decoder": true,
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