Instructions to use kiddothe2b/hierarchical-transformer-LC1-mini-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiddothe2b/hierarchical-transformer-LC1-mini-1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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datasets:
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- wikipedia
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model-index:
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- name: kiddothe2b/
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results: []
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---
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# Hierarchical Attention Transformer (HAT) /
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## Model description
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```python
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from transformers import pipeline
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mlm_model = pipeline('fill-mask', model='kiddothe2b/
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mlm_model("Hello I'm a <mask> model.")
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```
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```python
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from transformers import AutoTokenizer, AutoModelforSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/
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doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/
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```
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## Limitations and bias
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datasets:
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- wikipedia
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model-index:
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- name: kiddothe2b/hierarchical-transformer-LC1-mini-1024
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results: []
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---
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# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-LC1-mini-1024
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## Model description
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```python
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from transformers import pipeline
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mlm_model = pipeline('fill-mask', model='kiddothe2b/hierarchical-transformer-LC1-mini-1024', trust_remote_code=True)
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mlm_model("Hello I'm a <mask> model.")
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```
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```python
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from transformers import AutoTokenizer, AutoModelforSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
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doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hierarchical-transformer-LC1-mini-1024', trust_remote_code=True)
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```
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## Limitations and bias
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