### `AutoModelForSequenceClassification` 

**Class:** `AutoModelForSequenceClassification`

This is a generic model class designed to instantiate a specific model for sequence classification (with a classification head) based on a configuration or pretrained weights. It is used with `from_pretrained()` or `from_config()` methods to load models.

#### Key Points:
- **Instantiation:** This class cannot be instantiated directly using the `__init__()` method; it should be loaded using `from_pretrained()` or `from_config()` methods.
- **Purpose:** Loads models like BERT, RoBERTa, GPT, etc., with a sequence classification head.

---

### Methods

#### `from_config(config: PretrainedConfig, **kwargs)`

This method loads a model from a configuration. It selects the model class based on the configuration file provided. The models include ALBERT, BART, BERT, etc.

**Parameters:**
- `config (PretrainedConfig)`: The configuration class, which determines the model to load (e.g., `BertConfig`, `DistilBertConfig`).
  
**Example:**
```python
from transformers import AutoConfig, AutoModelForSequenceClassification

# Download and load configuration
config = AutoConfig.from_pretrained('bert-base-cased')
model = AutoModelForSequenceClassification.from_config(config)
```

---

#### `from_pretrained(pretrained_model_name_or_path, **model_args, **kwargs)`

This method loads a model from pretrained weights or a local directory containing model weights. It can also handle TensorFlow checkpoints.

**Parameters:**
- `pretrained_model_name_or_path (str or os.PathLike)`: The model's name or path to the pretrained model.
- `model_args (additional positional arguments)`: Passed to the model's `__init__()` method.
- `config (PretrainedConfig, optional)`: Custom configuration to use for the model.
- `state_dict (Dict[str, torch.Tensor], optional)`: If you want to use your own weights instead of pretrained ones.
- `from_tf (bool, optional)`: Set to `True` to load weights from a TensorFlow checkpoint.
- `cache_dir (str, optional)`: Custom cache directory for storing the model.

**Example:**
```python
from transformers import AutoModelForSequenceClassification

# Load pretrained model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-cased')

# Load with updated configuration
model = AutoModelForSequenceClassification.from_pretrained('bert-base-cased', output_attentions=True)
print(model.config.output_attentions)  # True
```

#### Key Model Types:
The model class selected is based on the configuration. For instance:
- **ALBERT**: `AlbertForSequenceClassification`
- **BERT**: `BertForSequenceClassification`
- **RoBERTa**: `RobertaForSequenceClassification`
- **DistilBERT**: `DistilBertForSequenceClassification`
- **XLNet**: `XLNetForSequenceClassification`
- Other models: GPT2, GPT-Neo, T5, etc.

The model is set to evaluation mode (`model.eval()`) by default, so you need to set it back to training mode (`model.train()`) for training.

--- 

#### Example of Loading a Model:
```python
from transformers import AutoModelForSequenceClassification

# Download and load the pretrained model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-cased')

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