[Almost all TF models] TF clean up: add missing CLM / MLM loss; fix T5 naming and keras compile (#5395)

* add first version of clm tf

* make style

* add more tests for bert

* update tf clm loss

* fix tests

* correct tf ner script

* add mlm loss

* delete bogus file

* clean tf auto model + add tests

* finish adding clm loss everywhere

* fix training in distilbert

* fix flake8

* save intermediate

* fix tf t5 naming

* remove prints

* finish up

* up

* fix tf gpt2

* fix new test utils import

* fix flake8

* keep backward compatibility

* Update src/transformers/modeling_tf_albert.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_auto.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_electra.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_roberta.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_mobilebert.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_auto.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_bert.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/modeling_tf_distilbert.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* apply sylvains suggestions

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Patrick von Platen
2020-07-07 18:15:53 +02:00
committed by GitHub
parent 33e43edddc
commit 4dc65591b5
23 changed files with 1516 additions and 315 deletions

View File

@@ -17,6 +17,7 @@
import logging
import os
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
@@ -184,7 +185,12 @@ def main():
for i in range(batch_size):
for j in range(seq_len):
if label_ids[i, j] != -1:
if label_ids[i, j] == -1:
label_ids[i, j] = -100
warnings.warn(
"Using `-1` to mask the loss for the token is depreciated. Please use `-100` instead."
)
if label_ids[i, j] != -100:
out_label_list[i].append(label_map[label_ids[i][j]])
preds_list[i].append(label_map[preds[i][j]])