Update README.md

README.md

@@ -8,13 +8,13 @@ tags: []
 ### Model Description
 Fine-tuned model for analysing cryptocurrency news for the following crypto coins:
 Bitcoin, Ethereum, Tether, Solana, Binance Coin. Max input size for texts is 1024 tokens that is about 
-3.5K chars of texts. Model outputs short text summary and list coins for uptrend and downtrend.
+3.5K chars of texts. Model is created by fine-tuning facebook/bart-large transformer model. 
+Model outputs short text summary and uptrend/downtrend lists of crypto coins.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model:
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-summarizer = pipeline("summarization", model = "bpavlsh/bart-crypto-summary")
+---
 summarizer = pipeline("summarization", model = "bpavlsh/bart-crypto-summary")
 txt="""
 Crypto market shows mixed signals. Bitcoin (BTC) and Ethereum (ETH) is experiencing a slight downturn, weighed down by bearish 
@@ -22,123 +22,12 @@ investor sentiment, while Solana (SOL) see sharp uptrends driven by increased on
 """
 result=summarizer(txt, early_stopping=True)[0]['summary_text']
 print(result)
-
+---
 Result:
 Bitcoin and Ethereum are experiencing a slight downturn with bearish investor sentiment, while Solana shows a strong uptrend driven by increased on-chain activity. 
 Uptrend: Solana.
 Downtrend: Bitcoin, Ethereum.
 
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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 ## Disclaimer
 We are sharing a considered approach, ideas and results for academic purpose only, 
 not any financial advice or recommendations for real business or investment.