# RepositoryNotFoundError: 404 Client Error
**URL:**
**Author:**  [@akshat3492](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fakshat3492)%5C
**Post date:** [August 31, 2023, 3:19am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Frepositorynotfounderror-404-client-error%2F52908%2F1 "2023-08-31T03:19:10Z")
Hi everyone, I am new to NLP and working with HuggingFace. I am working on a text summarization project and trying to fine tune the model. Below is the code I wrote but I am getting the error which I am not able to solve. Any leads would be appreciated.
```auto
from huggingface_hub import notebook_login
notebook_login()
```
I passed here my huggingface token…
```auto
from transformers import Seq2SeqTrainingArguments
batch_size = 8
num_train_epochs = 8
# Show the training loss with every epoch
logging_steps = len(tokenized_datasets["article"]) // batch_size
model_name = model_checkpoint.split("/")[-1]
args = Seq2SeqTrainingArguments(
output_dir="https://huggingface.co/username/mT5",
evaluation_strategy="epoch",
learning_rate=5.6e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=num_train_epochs,
predict_with_generate=True,
logging_steps=logging_steps,
push_to_hub=True
)
```
```auto
def compute_metrics(eval_pred):
predictions, labels = eval_pred
# Decode generated summaries into text
decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
# Replace -100 in the labels as we can't decode them
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
# Decode reference summaries into text
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# ROUGE expects a newline after each sentence
decoded_preds = ["\n".join(sent_tokenize(pred.strip())) for pred in decoded_preds]
decoded_labels = ["\n".join(sent_tokenize(label.strip())) for label in decoded_labels]
# Compute ROUGE scores
result = rouge.compute(
predictions=decoded_preds, references=decoded_labels, use_stemmer=True
)
# Extract the median scores
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
return {k: round(v, 4) for k, v in result.items()}
```
```auto
from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
from transformers import Seq2SeqTrainer
trainer = Seq2SeqTrainer(
model = model,
args = args,
train_dataset=tokenized_datasets["article"],
eval_dataset=test_tokenized_datasets["article"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
```
Here is the final error I am getting…
RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-64f004f3-5195d9d41d468e89023d924f;a7ed83d8-a09f-494e-ae69-8623b7517abb)
Repository Not Found for url: [https://huggingface.co/api/models/mT5](https://huggingface.co/api/models/mT5).
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
---
**Author:**  [@vpkprasanna](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fvpkprasanna)%5C
**Post date:** [August 31, 2023, 5:57am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Frepositorynotfounderror-404-client-error%2F52908%2F2 "2023-08-31T05:57:22Z")
One issue which i found in argument `output_dir` of `Seq2SeqTrainingArguments` is it should be your local path rather than remote path and you cannot use a remote path over here.
This output directory helps us to save the model checkpoints and other stuffs .
[see the docs of Training Arguments](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_bart_dlm_flax.py#L71)
---
**Author:**  [@akshat3492](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fakshat3492)%5C
**Post date:** [August 31, 2023, 7:50pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Frepositorynotfounderror-404-client-error%2F52908%2F3 "2023-08-31T19:50:14Z")
Thanks for the suggestion. Along with the solution you mentioned, another issue was that the access token had just read access whereas we need to provide write access.