[tune](deps): Bump transformers from 4.8.1 to 4.9.2 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.8.1 to 4.9.2.

Release notes

Sourced from transformers's releases.

v4.9.2: Patch release

• Tpu tie weights #13030 (@​sgugger)
• ONNX fixes & examples: #13048, #13049, #13028, #13014, #12911, (@​mfuntowicz, @​michaelbenayoun, @​LysandreJik)
• Fix push_to_hub for TPUs #12895 (@​sgugger)

v4.9.1: Patch release

Fix barrier for SM distributed #12853 (@​sgugger)

v4.9.0: TensorFlow examples, CANINE, tokenizer training, ONNX rework

ONNX rework

This version introduces a new package, transformers.onnx, which can be used to export models to ONNX. Contrary to the previous implementation, this approach is meant as an easily extendable package where users may define their own ONNX configurations and export the models they wish to export.

python -m transformers.onnx --model=bert-base-cased onnx/bert-base-cased/

Validating ONNX model...
        -[✓] ONNX model outputs' name match reference model ({'pooler_output', 'last_hidden_state'}
        - Validating ONNX Model output "last_hidden_state":
                -[✓] (2, 8, 768) matchs (2, 8, 768)
                -[✓] all values close (atol: 0.0001)
        - Validating ONNX Model output "pooler_output":
                -[✓] (2, 768) matchs (2, 768)
                -[✓] all values close (atol: 0.0001)
All good, model saved at: onnx/bert-base-cased/model.onnx

• [RFC] Laying down building stone for more flexible ONNX export capabilities #11786 (@​mfuntowicz)

CANINE model

Four new models are released as part of the CANINE implementation: CanineForSequenceClassification, CanineForMultipleChoice, CanineForTokenClassification and CanineForQuestionAnswering, in PyTorch.

The CANINE model was proposed in CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It’s among the first papers that train a Transformer without using an explicit tokenization step (such as Byte Pair Encoding (BPE), WordPiece, or SentencePiece). Instead, the model is trained directly at a Unicode character level. Training at a character level inevitably comes with a longer sequence length, which CANINE solves with an efficient downsampling strategy, before applying a deep Transformer encoder.

• Add CANINE #12024 (@​NielsRogge)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=canine

Tokenizer training

This version introduces a new method to train a tokenizer from scratch based off of an existing tokenizer configuration.

from datasets import load_dataset
from transformers import AutoTokenizer
</tr></table> 

... (truncated)

Commits

• 41981a2 Patch release: v4.9.2
• ec78422 Tpu tie weights (#13030)
• bfd5354 Add to ONNX docs (#13048)
• 226763a Add MBART to models exportable with ONNX (#13049)
• f595ea3 Put smaller ALBERT model (#13028)
• a12fa50 T5 with past ONNX export (#13014)
• 94b7db9 GPT-Neo ONNX export (#12911)
• 2c255a2 Fix push_to_hub for TPUs (#12895)
• ca272fc ONNX v2 raises an Exception when using PyTorch < 1.8.0 (#12933)
• bff1c71 Release: v4.9.1
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[dependabot]

Superseded by #56.