gpt2_nli_rte_pfeiffer.yaml

# Adapter-Hub adapter entry
# Defines a single adapter entry in Adapter-Hub
# --------------------

# The type of adapter (one of the options available in `adapter_type`.
type: text_task

# The string identifier of the task this adapter belongs to.
task: nli

# The string identifier of the subtask this adapter belongs to.
subtask: rte

# The model type.
# Example: bert
model_type: gpt2

# The string identifier of the pre-trained model (by which it is identified at Huggingface).
# Example: bert-base-uncased
model_name: gpt2

# The name of the author(s) of this adapter.
author: Hannah Sterz

# Describes the adapter architecture used by this adapter
config:
  # The name of the adapter config used by this adapter (a short name available in the `architectures` folder).
  # Example: pfeiffer
  using: pfeiffer
  non_linearity: relu
  reduction_factor: 16
default_version: '1'

# A list of different versions of this adapter available for download.
files:
- version: '1'
  url: https://public.ukp.informatik.tu-darmstadt.de/AdapterHub/v2/rte/gpt2/gpt2_nli_rte_pfeiffer.zip
  sha1: 8078688bc7f29e639256f4d93ad0a26934f3d509
  sha256: 1f9de887d23021368021fd8b92a167b3fcfae846e6076176caa4f4a2bf40395e

# (optional) A short description of this adapter.
description: 'Adapter for gpt2 in Pfeiffer architecture trained on the RTE dataset for 10 epochs with a learning rate of 1e-4.'

# (optional) A contact email of the author(s).
email: hannah.sterz@stud.tu-darmstadt.de

# (optional) The name of the model class from which this adapter was extracted. This field is mainly intended for adapters with prediction heads.
# Example: BertModelWithHeads
model_class: GPT2ForSequenceClassification

# (optional) If the adapter has a pre-trained prediction head included.
prediction_head: true

# (optional) A Twitter handle associated with the author(s).
twitter: '@h_sterz'