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Update Neptune docs (#22452)

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examples/pytorch/README.md
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@ -262,13 +262,13 @@ First, install the Neptune client library. You can do it with either `pip` or `c
`pip`:
```bash
pip install neptune-client
pip install neptune
```
`conda`:
```bash
conda install -c conda-forge neptune-client
conda install -c conda-forge neptune
```
Next, in your model training script, import `NeptuneCallback`:
@ -283,8 +283,8 @@ To enable Neptune logging, in your `TrainingArguments`, set the `report_to` argu
training_args = TrainingArguments(
"quick-training-distilbert-mrpc",
evaluation_strategy="steps",
eval_steps = 20,
report_to = "neptune",
eval_steps=20,
report_to="neptune",
)
trainer = Trainer(
@ -294,6 +294,8 @@ trainer = Trainer(
)
```
**Note:** This method requires saving your Neptune credentials as environment variables (see the bottom of the section).
Alternatively, for more logging options, create a Neptune callback:
```python
@ -318,7 +320,7 @@ neptune_callback = NeptuneCallback(
Pass the callback to the Trainer:
```python
training_args = TrainingArguments(..., report_to = None)
training_args = TrainingArguments(..., report_to=None)
trainer = Trainer(
model,
training_args,
@ -336,7 +338,7 @@ Now, when you start the training with `trainer.train()`, your metadata will be l
| `NEPTUNE_API_TOKEN` | Your Neptune API token. To find and copy it, click your Neptune avatar and select **Get your API token**. |
| `NEPTUNE_PROJECT` | The full name of your Neptune project (`workspace-name/project-name`). To find and copy it, head to **project settings** → **Properties**. |
For detailed instructions and examples, see the [Neptune docs](https://docs.neptune.ai/integrations-and-supported-tools/model-training/hugging-face).
For detailed instructions and examples, see the [Neptune docs](https://docs.neptune.ai/integrations/transformers/).
### ClearML
@ -373,4 +375,4 @@ Advanced configuration is possible by setting environment variables:
| CLEARML_PROJECT | Name of the project in ClearML. (default: `"HuggingFace Transformers"`) |
| CLEARML_TASK | Name of the task in ClearML. (default: `"Trainer"`) |
Additional configuration options are available through generic [clearml environment variables](https://clear.ml/docs/latest/docs/configs/env_vars).
Additional configuration options are available through generic [clearml environment variables](https://clear.ml/docs/latest/docs/configs/env_vars).