naver-ai/rdnet_tiny.nv_in1k
Image Classification
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Park JuHoon
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aifeifei798's
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π This program is designed to remove emojis from a given text. It uses a regular expression (regex) pattern to match and replace emojis with an empty string, effectively removing them from the text. The pattern includes a range of Unicode characters that correspond to various types of emojis, such as emoticons, symbols, and flags. By using this program, you can clean up text data by removing any emojis that may be present, which can be useful for text processing, analysis, or other applications where emojis are not desired. π»
import re
def remove_emojis(text):
# Define a broader emoji pattern
emoji_pattern = re.compile(
"["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
u"\U0001F900-\U0001F9FF" # supplemental symbols and pictographs
u"\U0001FA00-\U0001FA6F" # chess symbols and more emojis
u"\U0001FA70-\U0001FAFF" # more symbols and pictographs
u"\U00002600-\U000026FF" # miscellaneous symbols
u"\U00002B50-\U00002B59" # additional symbols
u"\U0000200D" # zero width joiner
u"\U0000200C" # zero width non-joiner
u"\U0000FE0F" # emoji variation selector
"]+", flags=re.UNICODE
)
return emoji_pattern.sub(r'', text)
reacted to
Kseniase's
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15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> Effective Approaches to Attention-based Neural Machine Translation (1508.04025)
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> Attention Is All You Need (1706.03762)
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2104.08771)
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> Attention Is All You Need (1706.03762)
Multiple attention βheadsβ are run in parallel.β The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2405.04434)
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> End-To-End Memory Networks (1503.08895)
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments π
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> Effective Approaches to Attention-based Neural Machine Translation (1508.04025)
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> Attention Is All You Need (1706.03762)
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2104.08771)
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> Attention Is All You Need (1706.03762)
Multiple attention βheadsβ are run in parallel.β The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2405.04434)
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> End-To-End Memory Networks (1503.08895)
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments π
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We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!
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