OverflowError: can't convert negative int to unsigned[finetuning XLNet] 
Author: ZHAOFEGNSHUN
Pull request: no

### System Info

  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/sentence_transformers/SentenceTransformer.py", line 592, in tokenize
    return self._first_module().tokenize(texts, **kwargs)
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/sentence_transformers/models/Transformer.py", line 146, in tokenize
    self.tokenizer(
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/transformers/tokenization_utils_base.py", line 2858, in __call__
    encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/transformers/tokenization_utils_base.py", line 2944, in _call_one
    return self.batch_encode_plus(
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/transformers/tokenization_utils_base.py", line 3135, in batch_encode_plus
    return self._batch_encode_plus(
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/transformers/tokenization_utils_fast.py", line 496, in _batch_encode_plus
    self.set_truncation_and_padding(
  File "/home/luban/.conda/envs/my/lib/python3.9/site-packages/transformers/tokenization_utils_fast.py", line 451, in set_truncation_and_padding
    self._tokenizer.enable_truncation(**target)
OverflowError: can't convert negative int to unsigned


### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction
```python
from torch.utils.data import DataLoader
import math
from sentence_transformers import SentenceTransformer, LoggingHandler, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.readers import InputExample
import logging
from datetime import datetime
import sys
import os
import gzip
import csv
from transformers import AutoTokenizer
from transformers import XLNetTokenizer
import logging
logging.getLogger().setLevel(logging.INFO)


model_name = sys.argv[1] if len(sys.argv) > 1 else "/nfs/XLNet/XLNet/xlnet-base-cased"



train_batch_size = 4
num_epochs = 4
model_save_path = (
    "output/training_stsbenchmark_" + model_name.replace("/", "-") + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)

word_embedding_model = models.Transformer(model_name)

pooling_model = models.Pooling(
    word_embedding_model.get_word_embedding_dimension(),
    pooling_mode_mean_tokens=True,
    pooling_mode_cls_token=False,
    pooling_mode_max_tokens=False,
)

model = SentenceTransformer(modules=[word_embedding_model, pooling_model])




train_samples = []
dev_samples = []

with open('/nfs/XLNet/XLNet/al.csv', "r", encoding="utf-8") as csvfile:
    reader = csv.DictReader(csvfile)
    
    for row in reader:
        score = float(row["score"]) / 5.0 
        sentence1 = row["sentence1"]
        sentence2 = row["sentence2"]
      

        inp_example = InputExample(texts=[sentence1, sentence2], label=score)
        train_samples.append(inp_example)
        dev_samples.append(inp_example)



train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
train_loss = losses.CosineSimilarityLoss(model=model)

evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name="sts-dev")


warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1)  
print("Warmup-steps: {}".format(warmup_steps))



model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    
    epochs=num_epochs,
    
    warmup_steps=warmup_steps,
    output_path=model_save_path,
    save_best_model = True,
)
```

### Expected behavior

I hope it can run

Comments:
Hi @ZHAOFEGNSHUN, thanks for raising an issue! 

This is a question best placed in our [forums](https://discuss.huggingface.co/). We try to reserve the github issues for feature requests and bug reports.

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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