Fill-Mask
Transformers
Safetensors
English
deberta-v2
babylm
deberta-v3
masked-lm
visual-grounding
data-efficient-pretraining
vision-init-sam
Instructions to use augustinian-babylm/deberta-base-100k-sam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use augustinian-babylm/deberta-base-100k-sam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="augustinian-babylm/deberta-base-100k-sam")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("augustinian-babylm/deberta-base-100k-sam") model = AutoModelForMaskedLM.from_pretrained("augustinian-babylm/deberta-base-100k-sam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint_map.json from augustinian-babylm/deberta-base-100k-sam: direct link, hf CLI and curl.
- Browser
- Download file 3.88 kB
-
https://huggingface.co/augustinian-babylm/deberta-base-100k-sam/resolve/main/checkpoint_map.json
- Command line
-
hf download hf://augustinian-babylm/deberta-base-100k-sam/checkpoint_map.json
-
curl -L -o checkpoint_map.json https://huggingface.co/augustinian-babylm/deberta-base-100k-sam/resolve/main/checkpoint_map.json
3.88 kB
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| "target_words": 1000000, | |
| "ideal_step": 259, | |
| "mapped_step": 256, | |
| "mapped_branch": "step256", | |
| "words_at_mapped_step": 986986, | |
| "relative_word_error": 0.013 | |
| }, | |
| "chck_2M": { | |
| "target_words": 2000000, | |
| "ideal_step": 519, | |
| "mapped_step": 512, | |
| "mapped_branch": "step512", | |
| "words_at_mapped_step": 1973972, | |
| "relative_word_error": 0.013 | |
| }, | |
| "chck_3M": { | |
| "target_words": 3000000, | |
| "ideal_step": 778, | |
| "mapped_step": 1000, | |
| "mapped_branch": "step1000", | |
| "words_at_mapped_step": 3855414, | |
| "relative_word_error": 0.2851 | |
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| "mapped_branch": "step1000", | |
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| "relative_word_error": 0.0361 | |
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