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  - bookcorpus
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  - bookcorpusopen
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  - nRuaif/OpenOrca-GPT3.5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  language:
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  - en
 
 
 
 
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  metrics:
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  - accuracy
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  - bertscore
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  - mean_iou
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  tags:
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  - code
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Data 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 Data 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]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **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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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
15
  - bookcorpus
16
  - bookcorpusopen
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  - nRuaif/OpenOrca-GPT3.5
18
+ - irds/codesearchnet
19
+ - giganticode/java-cmpx-v1
20
+ - nickrosh/Evol-Instruct-Code-80k-v1
21
+ - bigcode/starcoderdata
22
+ - bigcode/the-stack
23
+ - bigcode/the-stack-smol
24
+ - Cdaprod/AI-Developer-Prompts
25
+ - code_x_glue_ct_code_to_text
26
+ - codeparrot/github-code
27
+ - codeparrot/github-code-clean
28
+ - code_x_glue_cc_code_completion_line
29
+ - >-
30
+ autoevaluate/autoeval-eval-jeffdshen__inverse_superglue_mixedp1-jeffdshen__inverse-63643c-1665558893
31
+ - bentrevett/multi30k
32
+ - edbeeching/decision_transformer_gym_replay
33
+ - psyche/common_crawl
34
+ - Birchlabs/openai-prm800k-solutions-only
35
+ - openchat/openchat_sharegpt4_dataset
36
+ - Open-Orca/OpenOrca
37
+ - cjvt/slownet
38
+ - para_crawl
39
+ - zeroshot/twitter-financial-news-sentiment
40
+ - laugustyniak/political-advertising-pl
41
+ - code_search_net
42
+ - sukaka/novelai-webui
43
+ - P1ayer-1/chatgpt-conversations-chatlogs.net
44
+ - daniel2588/sarcasm
45
+ - psmathur/orca_minis_uncensored_dataset
46
+ - player1537/Bloom-560m-trained-on-Wizard-Vicuna-Uncensored-trained-on-Based
47
+ - shahules786/prosocial-nsfw-reddit
48
+ - Thewillonline/reddit-sarcasm
49
+ - datasciencemmw/current-data
50
+ - Oniichat/bluemoon_roleplay_chat_data_300k_messages
51
+ - dell-research-harvard/AmericanStories
52
+ - b-mc2/sql-create-context
53
+ - rahulmallah/autotrain-data-emotion-detection
54
+ - theblackcat102/multiround-programming-convo
55
+ - Lsavints/software_knowledgebase
56
+ - RazinAleks/SO-Python_QA-Web_Development_class
57
+ - codeparrot/apps
58
+ - vlsp-2023-vllm/en-to-vi-formal-informal-tranlations
59
+ - fraug-library/english_contractions_extensions
60
+ - spencer/software_slacks
61
+ - Abirate/english_quotes
62
+ - Nexdata/American_English_Natural_Dialogue_Speech_Data
63
+ - Nexdata/Latin_American_Speaking_English_Speech_Data_by_Mobile_Phone
64
+ - Nexdata/American_English_Speech_Data_by_Mobile_Phone_Reading
65
+ - Nexdata/American_English_Speech_Synthesis_Corpus-Female
66
+ - rombodawg/LimitlessCodeTraining
67
+ - RikoteMaster/Emotion_Recognition_4_llama2
68
+ - Villian7/Emotions_Data
69
+ - alanland/llama2-self-cognition
70
+ - CognitiveScience/coscidata
71
+ - bibidentuhanoi/gideon_self_cognition
72
+ - gollark/consciousness
73
+ - juletxara/visual-spatial-reasoning
74
+ - lintang/numerical_reasoning_arithmetic
75
+ - reasoning-machines/gsm-hard
76
+ - open-source-metrics/reinforcement-learning-checkpoint-downloads
77
+ - igbo_english_machine_translation
78
+ - US-Artificial-Intelligence/algemap
79
+ - rombodawg/2XUNCENSORED_alpaca_840k_Evol_USER_ASSIS
80
+ - griffin/chain_of_density
81
+ - >-
82
+ shirsh10mall/LLM_Instruct_Learning_Project_Preprocessed_Tokenized_Open_Orca_Dataset_Flan_T5
83
+ - Thaweewat/chain-of-thought-74k-th
84
+ - AlekseyKorshuk/chain-of-thoughts-chatml-deduplicated
85
+ - dair-ai/emotion
86
+ - hita/social-behavior-emotions
87
+ - Bingsu/Human_Action_Recognition
88
+ - anjandash/java-8m-methods-v1
89
+ - nadiamaqbool81/java_code_instructions_1.178k_alpaca
90
+ - DavidMOBrien/8000-java
91
+ - rombodawg/LimitlessCodeTraining_1k-Python-Javascript_GuanacoFormat
92
+ - angie-chen55/javascript-github-code
93
+ - kye/all-lucidrain-python-3
94
+ - Fraser/python-state-changes
95
+ - ammarnasr/the-stack-ruby-clean
96
+ - ammarnasr/the-stack-rust-clean
97
+ - seyyedaliayati/solidity-dataset
98
+ - jkhedri/psychology-dataset
99
+ - KonradSzafer/stackoverflow_linux
100
+ - vikp/textbook_quality_programming
101
+ - rombodawg/LosslessMegaCodeTrainingV3_MINI
102
+ - BelleGroup/multiturn_chat_0.8M
103
+ - smangrul/code-chat-assistant-v1
104
+ - goendalf666/sales-textbook_for_convincing_and_selling
105
+ - readerbench/ConversationalAgent-Ro
106
+ - beurkinger/autotrain-data-human-action-recognition
107
+ - jpwahle/autoencoder-paraphrase-dataset
108
+ - jpwahle/autoregressive-paraphrase-dataset
109
+ - teknium/GPT4-LLM-Cleaned
110
+ - Anthropic/model-written-evals
111
+ - openai_humaneval
112
+ - kye/all-google-ai-python-code
113
+ - kye/all-openai-github-code
114
+ - EleutherAI/lambada_openai
115
+ - CShorten/ML-ArXiv-Papers
116
+ - WaltonFuture/InstructionGPT-4
117
+ - open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B
118
+ - seansullivan/INT-Business-Syllabus
119
+ - theoldmandthesea/17k_business_book
120
+ - SunRise228/business-doc
121
+ - gauravshrm211/VC-startup-evaluation-for-investment
122
+ - TuningAI/Startups_V1
123
+ - TuningAI/Startups_V2
124
+ - AdiOO7/llama-2-finance
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+ - scillm/scientific_papers
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+ - gokuls/wiki_book_corpus_complete_processed_bert_dataset
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+ - the_pile_books3
128
+ - go_emotions
129
+ - yizhongw/self_instruct
130
+ - codeparrot/self-instruct-starcoder
131
+ - Amani27/massive_translation_dataset
132
+ - huggingface/transformers-metadata
133
+ - hf-internal-testing/transformers-metadata
134
+ - commonsense_qa
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+ - nlplabtdtu/test-edu-crawl
136
+ - kernelmachine/open-license-corpus
137
+ - BDas/EnglishNLPDataset
138
+ - CyberNative/github_cybersecurity_READMEs
139
+ - thomwolf/github-python
140
+ - CM/codexglue_code2text_java
141
+ - autoevaluate/autoeval-staging-eval-project-glue-f16e6c43-14015917
142
+ - lemonteaa/algorithmic-reasoning-seed
143
+ - EmpathyFirstMedia/algolia
144
+ - vicgalle/alpaca-gpt4
145
+ - pariajm/sharif_emotional_speech_dataset
146
+ - lighteval/synthetic_reasoning_natural
147
+ - jxu124/llava_complex_reasoning_77k
148
+ - bibidentuhanoi/gideon_self_cognition_text
149
+ - ohilikeit/empathetic_dialogues_mutli_turn_ko
150
+ - KevinZ/psycholinguistic_eval
151
+ - fiveflow/psychology-dataset
152
+ - shahidul034/text_generation_model_data
153
+ - qwedsacf/story-generation
154
+ - EnigmaOfTheWorld/b-mc2-sql-create-context
155
+ - HuggingFaceH4/testing_self_instruct_small
156
+ - RUCAIBox/Data-to-text-Generation
157
+ - Fhrozen/AudioSet2K22
158
+ - Chr0my/Epidemic_sounds
159
+ - ChristophSchuhmann/lyrics-index
160
+ - Cropinky/rap_lyrics_english
161
+ - tsterbak/eurovision-lyrics-1956-2023
162
+ - brunokreiner/genius-lyrics
163
+ - google/MusicCaps
164
+ - ccmusic-database/music_genre
165
+ - Hyeon2/riffusion-musiccaps-dataset
166
+ - SamAct/autotrain-data-musicprompt
167
+ - Chr0my/Epidemic_music
168
+ - juliensimon/autonlp-data-song-lyrics
169
+ - Datatang/North_American_English_Speech_Data_by_Mobile_Phone_and_PC
170
+ - Chr0my/freesound.org
171
+ - teticio/audio-diffusion-256
172
+ - KELONMYOSA/dusha_emotion_audio
173
+ - Ar4ikov/iemocap_audio_text_splitted
174
+ - flexthink/ljspeech
175
+ - mozilla-foundation/common_voice_13_0
176
+ - facebook/voxpopuli
177
+ - SocialGrep/one-million-reddit-jokes
178
+ - breadlicker45/human-midi-rlhf
179
+ - breadlicker45/midi-gpt-music-small
180
+ - projectlosangeles/Los-Angeles-MIDI-Dataset
181
+ - huggingartists/epic-rap-battles-of-history
182
+ - SocialGrep/one-million-reddit-confessions
183
+ - shahules786/prosocial-nsfw-reddit
184
+ - Thewillonline/reddit-sarcasm
185
+ - autoevaluate/autoeval-eval-futin__guess-vi-4200fb-2012366606
186
+ - lmsys/chatbot_arena_conversations
187
+ - mozilla-foundation/common_voice_11_0
188
+ - mozilla-foundation/common_voice_4_0
189
+ - dell-research-harvard/AmericanStories
190
+ - zZWipeoutZz/insane_style
191
+ - mu-llama/MusicQA
192
+ - RaphaelOlivier/whisper_adversarial_examples
193
+ - huggingartists/metallica
194
+ - vldsavelyev/guitar_tab
195
+ - NLPCoreTeam/humaneval_ru
196
+ - seungheondoh/audioset-music
197
+ - gary109/onset-singing3_corpora_parliament_processed_MIR-ST500
198
+ - LDD5522/Rock_Vocals
199
+ - huggingartists/rage-against-the-machine
200
+ - huggingartists/chester-bennington
201
+ - huggingartists/logic
202
+ - cmsolson75/artist_song_lyric_dataset
203
+ - BhavyaMuni/artist-lyrics
204
+ - vjain/emotional_intelligence
205
+ - mhenrichsen/context-aware-splits
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  language:
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  - en
208
+ - es
209
+ - it
210
+ - ru
211
+ - la
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  metrics:
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  - accuracy
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  - bertscore
 
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  - mean_iou
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  tags:
221
  - code
222
+ - music
223
+ library_name: transformers
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  ---
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+ Model Overview
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+ SquanchNasty is a groundbreaking AI model that pushes the boundaries of natural language processing and understanding. It is designed to generate creative, coherent, and contextually relevant text based on user prompts. With its advanced neural network architecture and extensive training on diverse datasets, SquanchNasty can generate high-quality responses across various domains and tasks.
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+
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+ Intended Use
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+ SquanchNasty is intended to be used as a creative and innovative tool to assist users in generating text-based content. It can be employed for a wide range of applications, including but not limited to:
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+
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+ Creative Writing: SquanchNasty can help users in generating unique storylines, dialogue, and descriptive passages for creative writing projects.
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+ Content Generation: It can be used to generate engaging and informative articles, blog posts, social media captions, and other written content.
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+ Language Translation: SquanchNasty's language generation capabilities can be leveraged to facilitate translation services by generating accurate and contextually appropriate translations.
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+ Coding Assistance: The model can assist programmers by providing code snippets, explanations, and suggestions for various programming languages.
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+ Conversational Agents: SquanchNasty's ability to generate contextually relevant responses makes it suitable for use in chatbots and virtual assistants.
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+ Model Capabilities
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+ SquanchNasty is designed to provide users with remarkable text generation capabilities. It can:
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+
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+ Generate Coherent Text: The model produces text that is coherent, logical, and contextually relevant to the given prompt.
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+ Maintain Consistent Style: SquanchNasty can adapt its writing style to match different genres, tones, or formalities based on the provided input.
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+ Handle Open-Ended Prompts: The model can generate creative and imaginative responses even with minimal or incomplete prompts.
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+ Incorporate User Preferences: SquanchNasty can be fine-tuned to incorporate user preferences and biases, allowing for personalized text generation.
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+ Provide Varied Outputs: The model can generate multiple diverse outputs for a given prompt, allowing users to explore different possibilities.
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+ Dataset and Training
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+ SquanchNasty has been trained on a vast array of high-quality datasets from various domains, such as literature, code, conversations, and more. The training data includes open-source text, code repositories, question-and-answer platforms, books, and dialogue datasets. The model has undergone extensive pre-training and fine-tuning processes to ensure optimal performance and versatility.
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+ Ethical Considerations
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+ As an AI research scientist, I am committed to upholding ethical guidelines and responsible AI practices. It is crucial to consider the following ethical considerations when using SquanchNasty:
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+
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+ Bias Mitigation: Efforts have been made to reduce biases during training, but it is essential to evaluate and address any potential biases in the model's generated output.
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+ Fairness and Accountability: Users should be aware that SquanchNasty's responses are based on the data it has been trained on, and it may reflect the biases and viewpoints present in the training data.
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+ User Responsibility: Users should exercise caution and accountability when utilizing SquanchNasty's generated content, ensuring it aligns with ethical standards.
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+ Content Moderation: It is recommended to implement content moderation mechanisms to ensure that the generated text adheres to community guidelines and legal frameworks.
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+ Performance and Limitations
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+ SquanchNasty exhibits exceptional performance in generating coherent and contextually relevant text. However, it is important to consider the following limitations:
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+
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+ Context Sensitivity: The model may not always capture intricate contextual nuances, leading to occasional errors or inconsistent responses.
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+ Sensitivity to Input: SquanchNasty's output heavily relies on the quality and clarity of the input prompt. Ambiguous or misleading prompts may result in less accurate or unexpected responses.
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+ Over-Reliance on Training Data: The model's responses are based on patterns and information present in the training data. Therefore, it may struggle with generating text on topics or concepts that are underrepresented or absent in the training data.
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+ Lack of Real-Time Information: SquanchNasty does not have access to real-time data and may generate responses based on outdated or inaccurate information.
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+ Conclusion
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+ SquanchNasty is a remarkable and groundbreaking AI model that offers exceptional text generation capabilities. It has been trained on diverse datasets and exhibits the potential to revolutionize various domains, including creative writing, content generation, coding assistance, and conversational agents. While it showcases impressive performance, it is important to consider ethical guidelines, address biases, and be mindful of its limitations when utilizing SquanchNasty for specific use cases