
### AutoModelForTokenClassification

`AutoModelForTokenClassification` is a generic class in the Hugging Face `transformers` library that automatically selects the appropriate model class for token classification based on the configuration or the model name.

#### Class Definition:
```python
class transformers.AutoModelForTokenClassification
```

This class is instantiated using the `from_pretrained()` or `from_config()` methods and cannot be directly instantiated using `__init__()` (an error will be thrown). It is used for token classification tasks, such as named entity recognition (NER), part-of-speech tagging, etc.

---

### from_config

The `from_config()` method allows you to instantiate a model from a configuration object. This method helps in selecting the correct model class based on the configuration class. For instance:

- `AlbertConfig` → `AlbertForTokenClassification`
- `BertConfig` → `BertForTokenClassification`
- `GPT2Config` → `GPT2ForTokenClassification`
- And many more model classes are mapped similarly.

#### Parameters:
- `config (PretrainedConfig)` — The configuration object, which determines the model to instantiate. For example:
  - `AlbertConfig` → `AlbertForTokenClassification`
  - `BertConfig` → `BertForTokenClassification`
  
**Example:**
```python
from transformers import AutoConfig, AutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained('bert-base-cased')
model = AutoModelForTokenClassification.from_config(config)
```

---

### from_pretrained

The `from_pretrained()` method allows you to instantiate a model from a pre-trained model or from a directory containing model weights. This method can accept a variety of arguments, including model ID, path to saved weights, and configuration parameters.

#### Parameters:
- `pretrained_model_name_or_path (str or os.PathLike)` — Path or model ID to the pre-trained model or checkpoint.
  - Example: `'bert-base-uncased'` or a local directory like `'./my_model_directory/'`.
  
- `model_args (additional positional arguments)` — Extra arguments passed to the model's `__init__` method.
  
- `config (PretrainedConfig, optional)` — Configuration to use instead of auto-loading it.
  
- `state_dict (Dict[str, torch.Tensor], optional)` — Custom state dictionary (useful if you want to load your own weights).
  
- `from_tf (bool, optional, defaults to False)` — Flag to load weights from a TensorFlow checkpoint instead of PyTorch.
  
- `force_download (bool, optional)` — Whether to force the re-download of the model if cached versions exist.
  
- `output_loading_info (bool, optional)` — Whether to return information about missing or unexpected keys when loading.

#### Example:
```python
from transformers import AutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTokenClassification.from_pretrained('bert-base-cased')

# Update configuration during loading
model = AutoModelForTokenClassification.from_pretrained('bert-base-cased', output_attentions=True)
print(model.config.output_attentions)  # Should print: True

# Loading from a TensorFlow checkpoint (slower)
config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
model = AutoModelForTokenClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
```

---

### Key Points:
- **Dynamic Model Selection:** The model class (`BertForTokenClassification`, `RoBERTaForTokenClassification`, etc.) is dynamically chosen based on the model configuration.
- **Evaluation Mode:** The model is by default in evaluation mode (`model.eval()`) after loading. To train the model, you need to set it to training mode with `model.train()`.
- **Loading Weights:** If loading from a TensorFlow checkpoint, you should pass `from_tf=True`, and a configuration object is required.

---
