CryptoBERT (code, models, paper)

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+Bitcoin's[[:space:]]Edge.[[:space:]]Embedded[[:space:]]Sentiment[[:space:]]in[[:space:]]Blockchain[[:space:]]Transactional[[:space:]]Data.pdf filter=lfs diff=lfs merge=lfs -text
+CBITS.[[:space:]]Crypto[[:space:]]BERT[[:space:]]Incorporated[[:space:]]Trading[[:space:]]System.pdf filter=lfs diff=lfs merge=lfs -text
+Sentiment[[:space:]]Classification[[:space:]]of[[:space:]]Cryptocurrency-Related[[:space:]]Social[[:space:]]Media[[:space:]]Posts.pdf filter=lfs diff=lfs merge=lfs -text

Bitcoin's Edge. Embedded Sentiment in Blockchain Transactional Data.pdf

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CBITS. Crypto BERT Incorporated Trading System.pdf

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Sentiment Classification of Cryptocurrency-Related Social Media Posts.pdf

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models/CryptoBERT (kk08)/.gitignore

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models/CryptoBERT (kk08)/README.md

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+---
+language:
+- en
+tags:
+- generated_from_trainer
+- crypto
+- sentiment
+- analysis
+pipeline_tag: text-classification
+base_model: ProsusAI/finbert
+model-index:
+- name: CryptoBERT
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# CryptoBERT
+
+This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the Custom Crypto Market Sentiment dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3823
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
+
+tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT")
+model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT")
+
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+text = "Bitcoin (BTC) touches $29k, Ethereum (ETH) Set To Explode, RenQ Finance (RENQ) Crosses Massive Milestone"
+result = classifier(text)
+print(result)
+
+```
+```
+[{'label': 'LABEL_1', 'score': 0.9678454399108887}]
+```
+## Model description
+
+This model fine-tunes the [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert), which is a pre-trained NLP model to analyze the sentiment of the financial text. 
+CryptoBERT model fine-tunes this by training the model as a downstream task on Custom Crypto Sentiment data to predict whether the given text related to the Crypto market is
+Positive (LABEL_1) or Negative (LABEL_0).
+
+## Intended uses & limitations
+
+The model can perform well on Crypto-related data. The main limitation is that the fine-tuning was done using only a small corpus of data
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 0.4077        | 1.0   | 27   | 0.4257          |
+| 0.2048        | 2.0   | 54   | 0.2479          |
+| 0.0725        | 3.0   | 81   | 0.3068          |
+| 0.0028        | 4.0   | 108  | 0.4120          |
+| 0.0014        | 5.0   | 135  | 0.3566          |
+| 0.0007        | 6.0   | 162  | 0.3495          |
+| 0.0006        | 7.0   | 189  | 0.3645          |
+| 0.0005        | 8.0   | 216  | 0.3754          |
+| 0.0004        | 9.0   | 243  | 0.3804          |
+| 0.0004        | 10.0  | 270  | 0.3823          |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3

models/CryptoBERT (kk08)/config.json

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+{
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+  "architectures": [
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+  ],
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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models/CryptoBERT (kk08)/source.txt

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models/CryptoBert (ElKulako)/README.md

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+---
+datasets:
+- ElKulako/stocktwits-crypto
+language:
+- en
+tags:
+- cryptocurrency
+- crypto
+- BERT
+- sentiment classification
+- NLP
+- bitcoin
+- ethereum
+- shib
+- social media
+- sentiment analysis
+- cryptocurrency sentiment analysis
+license: mit
+---
+
+For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689
+
+# CryptoBERT
+CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the [vinai's bertweet-base](https://huggingface.co/vinai/bertweet-base) language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. 
+(A research paper with more details will follow soon.)
+## Classification Training
+The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2
+
+CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto). 
+
+CryptoBERT was trained with a max sequence length of 128. Technically, it can handle sequences of up to 514 tokens, however, going beyond 128 is not recommended.
+
+# Classification Example
+```python
+from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
+model_name = "ElKulako/cryptobert"
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=64, truncation=True, padding = 'max_length')
+# post_1 & post_3 = bullish, post_2 = bearish
+post_1 = " see y'all tomorrow and can't wait to see ada in the morning, i wonder what price it is going to be at. 😎🐂🤠💯😴, bitcoin is looking good go for it and flash by that 45k. "
+post_2 = "  alright racers, it’s a race to the bottom! good luck today and remember there are no losers (minus those who invested in currency nobody really uses) take your marks... are you ready? go!!" 
+post_3 = " i'm never selling. the whole market can bottom out. i'll continue to hold this dumpster fire until the day i die if i need to." 
+df_posts = [post_1, post_2, post_3]
+preds = pipe(df_posts)
+print(preds)
+
+
+```
+
+```
+[{'label': 'Bullish', 'score': 0.8734585642814636}, {'label': 'Bearish', 'score': 0.9889495372772217}, {'label': 'Bullish', 'score': 0.6595883965492249}]
+```
+
+## Training Corpus
+CryptoBERT was trained on 3.2M social media posts regarding various cryptocurrencies. Only non-duplicate posts of length above 4 words were considered. The following communities were used as sources for our corpora:
+
+
+(1) StockTwits - 1.875M posts about the top 100 cryptos by trading volume. Posts were collected from the 1st of November 2021 to the 16th of June 2022. [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto)
+
+(2) Telegram - 664K posts from top 5 telegram groups: [Binance](https://t.me/binanceexchange), [Bittrex](https://t.me/BittrexGlobalEnglish), [huobi global](https://t.me/huobiglobalofficial), [Kucoin](https://t.me/Kucoin_Exchange), [OKEx](https://t.me/OKExOfficial_English). 
+Data from 16.11.2020 to 30.01.2021. Courtesy of [Anton](https://www.kaggle.com/datasets/aagghh/crypto-telegram-groups).
+
+(3) Reddit - 172K comments from various crypto investing threads, collected from May 2021 to May 2022
+
+(4) Twitter - 496K posts with hashtags XBT, Bitcoin or BTC. Collected for May 2018. Courtesy of [Paul](https://www.kaggle.com/datasets/paul92s/bitcoin-tweets-14m).

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models/CryptoBertStrong/README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

models/CryptoBertStrong/config.json

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+{
+  "_name_or_path": "CryptoBertStrong",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
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+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

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models/CryptoBertV2/README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

models/CryptoBertV2/config.json

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+}

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