Instructions to use WebOrganizer/TopicClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WebOrganizer/TopicClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WebOrganizer/TopicClassifier", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("WebOrganizer/TopicClassifier", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # coding=utf-8 | |
| # Copyright 2024 The GTE Team Authors and Alibaba Group. | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ NEW model configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class NewConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to | |
| instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the NEW | |
| [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"silu"` and `"gelu_new"` are supported. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (`int`, *optional*, defaults to 512): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| type_vocab_size (`int`, *optional*, defaults to 2): | |
| The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`]. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| position_embedding_type (`str`, *optional*, defaults to `"rope"`): | |
| Type of position embedding. Choose one of `"absolute"`, `"rope"`. | |
| rope_theta (`float`, *optional*, defaults to 10000.0): | |
| The base period of the RoPE embeddings. | |
| rope_scaling (`Dict`, *optional*): | |
| Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling | |
| strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is | |
| `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update | |
| `max_position_embeddings` to the expected new maximum. See the following thread for more information on how | |
| these scaling strategies behave: | |
| https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an | |
| experimental feature, subject to breaking API changes in future versions. | |
| classifier_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| Examples: | |
| ```python | |
| >>> from transformers import NewConfig, NewModel | |
| >>> # Initializing a NEW izhx/new-base-en style configuration | |
| >>> configuration = NewConfig() | |
| >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration | |
| >>> model = NewModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "new" | |
| def __init__( | |
| self, | |
| vocab_size=30528, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.0, | |
| max_position_embeddings=2048, | |
| type_vocab_size=1, | |
| initializer_range=0.02, | |
| layer_norm_type='layer_norm', | |
| layer_norm_eps=1e-12, | |
| # pad_token_id=0, | |
| position_embedding_type="rope", | |
| rope_theta=10000.0, | |
| rope_scaling=None, | |
| classifier_dropout=None, | |
| pack_qkv=True, | |
| unpad_inputs=False, | |
| use_memory_efficient_attention=False, | |
| logn_attention_scale=False, | |
| logn_attention_clip1=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_type = layer_norm_type | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.rope_theta = rope_theta | |
| self.rope_scaling = rope_scaling | |
| self.classifier_dropout = classifier_dropout | |
| self.pack_qkv = pack_qkv | |
| self.unpad_inputs = unpad_inputs | |
| self.use_memory_efficient_attention = use_memory_efficient_attention | |
| self.logn_attention_scale = logn_attention_scale | |
| self.logn_attention_clip1 = logn_attention_clip1 | |