def output_spec(self) -> Spec:
ret = {"tokens": lit_types.Tokens()}
ret["tokens_" + self.config.text_a_name] = lit_types.Tokens()
if self.config.text_b_name:
ret["tokens_" + self.config.text_b_name] = lit_types.Tokens()
if self.is_regression:
ret["score"] = lit_types.RegressionScore(
parent=self.config.label_name)
else:
ret["probas"] = lit_types.MulticlassPreds(
parent=self.config.label_name,
vocab=self.config.labels,
null_idx=self.config.null_label_idx)
ret["cls_emb"] = lit_types.Embeddings()
# Gradients, if requested.
if self.config.compute_grads:
ret["token_grad_" +
self.config.text_a_name] = lit_types.TokenGradients(
align="tokens_" + self.config.text_a_name)
if self.config.text_b_name:
ret["token_grad_" +
self.config.text_b_name] = lit_types.TokenGradients(
align="tokens_" + self.config.text_b_name)
# Attention heads, one field for each layer.
for i in range(self.model.config.num_hidden_layers):
ret[f"layer_{i}/attention"] = lit_types.AttentionHeads(
align=("tokens", "tokens"))
return ret
def output_spec(self) -> Spec:
ret = {"tokens": lit_types.Tokens()}
ret["tokens_" + self.config.text_a_name] = lit_types.Tokens(
parent=self.config.text_a_name)
if self.config.text_b_name:
ret["tokens_" + self.config.text_b_name] = lit_types.Tokens(
parent=self.config.text_b_name)
if self.is_regression:
ret["score"] = lit_types.RegressionScore(parent=self.config.label_name)
else:
ret["probas"] = lit_types.MulticlassPreds(
parent=self.config.label_name,
vocab=self.config.labels,
null_idx=self.config.null_label_idx)
ret["cls_emb"] = lit_types.Embeddings()
# Average embeddings, one per layer including embeddings.
for i in range(1 + self.model.config.num_hidden_layers):
ret[f"layer_{i}/avg_emb"] = lit_types.Embeddings()
ret["cls_grad"] = lit_types.Gradients(
grad_for="cls_emb", grad_target_field_key="grad_class")
# The input_embs_ and grad_class fields are used for Integrated Gradients.
ret["input_embs_" + self.config.text_a_name] = lit_types.TokenEmbeddings(
align="tokens_" + self.config.text_a_name)
if self.config.text_b_name:
ret["input_embs_" + self.config.text_b_name] = lit_types.TokenEmbeddings(
align="tokens_" + self.config.text_b_name)
# Gradients, if requested.
if self.config.compute_grads:
ret["grad_class"] = lit_types.CategoryLabel(required=False,
vocab=self.config.labels)
ret["token_grad_" + self.config.text_a_name] = lit_types.TokenGradients(
align="tokens_" + self.config.text_a_name,
grad_for="input_embs_" + self.config.text_a_name,
grad_target_field_key="grad_class")
if self.config.text_b_name:
ret["token_grad_" + self.config.text_b_name] = lit_types.TokenGradients(
align="tokens_" + self.config.text_b_name,
grad_for="input_embs_" + self.config.text_b_name,
grad_target_field_key="grad_class")
# Attention heads, one field for each layer.
for i in range(self.model.config.num_hidden_layers):
ret[f"layer_{i+1}/attention"] = lit_types.AttentionHeads(
align_in="tokens", align_out="tokens")
return ret
def output_spec(self) -> lit_types.Spec:
return {
"tokens": lit_types.Tokens(),
"logits": lit_types.RegressionScore(),
"cls_emb": lit_types.Embeddings(),
"token_grad_sentence": lit_types.TokenGradients(align="tokens")
}
def output_spec(self) -> lit_types.Spec:
return {
"tokens": lit_types.Tokens(),
"probas": lit_types.MulticlassPreds(parent="label", vocab=self._labels),
"cls_emb": lit_types.Embeddings(),
"token_grad_sentence": lit_types.TokenGradients(align="tokens")
}
def output_spec(self):
return {'probas': lit_types.MulticlassPreds(
parent='label',
vocab=['0', '1'],
null_idx=0),
'input_embs': lit_types.TokenEmbeddings(align='tokens'),
'input_embs_grad': lit_types.TokenGradients(align='tokens',
grad_for='input_embs',
grad_target='grad_class'
),
'tokens': lit_types.Tokens(),
'grad_class': lit_types.CategoryLabel(vocab=['0', '1'])
}
def output_spec(self) -> lit_types.Spec:
"""Give the output specifications."""
ret = {
"tokens": lit_types.Tokens(),
"probas": lit_types.MulticlassPreds(parent="label", vocab=self.LABELS),
"cls_emb": lit_types.Embeddings()
}
# Gradients, if requested.
if self.compute_grads:
ret["token_grad_sentence"] = lit_types.TokenGradients(align="tokens")
# Attention heads, one field for each layer.
for i in range(self.model.config.num_hidden_layers):
ret[f"layer_{i}/attention"] = lit_types.AttentionHeads(align=("tokens", "tokens"))
return ret
def output_spec(self) -> lit_types.Spec:
spec = {
"tokens":
lit_types.Tokens(),
"bio_tags":
lit_types.SequenceTags(align="tokens"),
"token_ids":
lit_types.SequenceTags(align="tokens"),
"grads":
lit_types.TokenGradients(align="tokens"),
"probas":
lit_types.MulticlassPreds(parent="bio_tags", vocab=self.LABELS)
}
for i in range(self.model.config.num_hidden_layers):
spec[f'layer_{i}/attention'] = lit_types.AttentionHeads(
align=("tokens", "tokens"))
return spec
