Token Classification
Transformers
PyTorch
Safetensors
English
albert
Generated from Trainer
Eval Results (legacy)
Instructions to use surrey-nlp/albert-large-v2-finetuned-abbDet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surrey-nlp/albert-large-v2-finetuned-abbDet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="surrey-nlp/albert-large-v2-finetuned-abbDet")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("surrey-nlp/albert-large-v2-finetuned-abbDet") model = AutoModelForTokenClassification.from_pretrained("surrey-nlp/albert-large-v2-finetuned-abbDet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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- recall
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- accuracy
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model-index:
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- name: albert-large-v2-finetuned-ner_with_callbacks
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results:
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- recall
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- f1
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- accuracy
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language:
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widget:
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- text: "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."
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- text: "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."
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- text: "Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI)."
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model-index:
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- name: albert-large-v2-finetuned-ner_with_callbacks
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results:
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