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bayartsogt/structbert-large	bayartsogt	2021-07-26T21:15:28Z	9	6	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "arxiv:1908.04577", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	# StructBERT: Un-Official Copy Official Repository Link: https://github.com/alibaba/AliceMind/tree/main/StructBERT Claimer * This model card is not produced by [AliceMind Team](https://github.com/alibaba/AliceMind/) ## Reproduce HFHub models: Download model/tokenizer vocab ```bash wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/large_bert_config.json && mv large_bert_config.json config.json wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/vocab.txt wget https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/en_model && mv en_model pytorch_model.bin ``` ```python from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer config = AutoConfig.from_pretrained("./config.json") model = AutoModelForMaskedLM.from_pretrained(".", config=config) tokenizer = AutoTokenizer.from_pretrained(".", config=config) model.push_to_hub("structbert-large") tokenizer.push_to_hub("structbert-large") ``` [https://arxiv.org/abs/1908.04577](https://arxiv.org/abs/1908.04577) # StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding ## Introduction We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. ## Pre-trained models \|Model \| Description \| #params \| Download \| \|------------------------\|-------------------------------------------\|------\|------\| \|structbert.en.large \| StructBERT using the BERT-large architecture \| 340M \| [structbert.en.large](https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/en_model) \| \|structroberta.en.large \| StructRoBERTa continue training from RoBERTa \| 355M \| Coming soon \| \|structbert.ch.large \| Chinese StructBERT; BERT-large architecture \| 330M \| [structbert.ch.large](https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/ch_model) \| ## Results The results of GLUE & CLUE tasks can be reproduced using the hyperparameters listed in the following "Example usage" section. #### structbert.en.large [GLUE benchmark](https://gluebenchmark.com/leaderboard) \|Model\| MNLI \| QNLIv2 \| QQP \| SST-2 \| MRPC \| \|--------------------\|-------\|-------\|-------\|-------\|-------\| \|structbert.en.large \|86.86% \|93.04% \|91.67% \|93.23% \|86.51% \| #### structbert.ch.large [CLUE benchmark](https://www.cluebenchmarks.com/) \|Model \| CMNLI \| OCNLI \| TNEWS \| AFQMC \| \|--------------------\|-------\|-------\|-------\|-------\| \|structbert.ch.large \|84.47% \|81.28% \|68.67% \|76.11% \| ## Example usage #### Requirements and Installation * [PyTorch](https://pytorch.org/) version >= 1.0.1 * Install other libraries via ``` pip install -r requirements.txt ``` * For faster training install NVIDIA's [apex](https://github.com/NVIDIA/apex) library #### Finetune MNLI ``` python run_classifier_multi_task.py \ --task_name MNLI \ --do_train \ --do_eval \ --do_test \ --amp_type O1 \ --lr_decay_factor 1 \ --dropout 0.1 \ --do_lower_case \ --detach_index -1 \ --core_encoder bert \ --data_dir path_to_glue_data \ --vocab_file config/vocab.txt \ --bert_config_file config/large_bert_config.json \ --init_checkpoint path_to_pretrained_model \ --max_seq_length 128 \ --train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --fast_train \ --gradient_accumulation_steps 1 \ --output_dir path_to_output_dir ``` ## Citation If you use our work, please cite: ``` @article{wang2019structbert, title={Structbert: Incorporating language structures into pre-training for deep language understanding}, author={Wang, Wei and Bi, Bin and Yan, Ming and Wu, Chen and Bao, Zuyi and Xia, Jiangnan and Peng, Liwei and Si, Luo}, journal={arXiv preprint arXiv:1908.04577}, year={2019} } ```
vasudevgupta/bigbird-roberta-base	vasudevgupta	2021-07-26T17:30:39Z	5	0	transformers	[ "transformers", "pytorch", "big_bird", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	Moved here: https://huggingface.co/google/bigbird-roberta-base
flax-community/gpt-neo-125M-code-clippy-dedup	flax-community	2021-07-26T14:07:29Z	6	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "gpt_neo", "text-generation", "arxiv:2107.03374", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# GPT-Neo-125M-Code-Clippy-Dedup > Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot ## Model Description PT-Neo-125M-Code-Clippy-Dedup is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on our deduplicated version of the Code Clippy Data dataset, which was scraped from public Github repositories (more information in the provided link). This model is specialized to autocomplete methods in multiple programming languages. ## Training data [Code Clippy Data dataset](https://huggingface.co/datasets/code_search_net). ## Training procedure In this model's training we tried to stabilize the training by limiting the types of files we were using to train to only those that contained file extensions for popular programming languages as our dataset contains other types of files as well such as `.txt` or project configuration files. We used the following extensions to filter by: The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_streaming_filter_flax.py). ```bash ./run_clm_streaming_filter_flax.py \ --output_dir $HOME/gpt-neo-125M-code-clippy-dedup \ --model_name_or_path="EleutherAI/gpt-neo-125M" \ --dataset_name $HOME/gpt-code-clippy/data_processing/code_clippy_filter.py \ --data_dir $HOME/code_clippy_data/code_clippy_dedup_data \ --text_column_name="text" \ --do_train --do_eval \ --block_size="2048" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="16" \ --preprocessing_num_workers="8" \ --learning_rate="1e-4" \ --max_steps 100000 \ --warmup_steps 2000 \ --decay_steps 30000 \ --adam_beta1="0.9" \ --adam_beta2="0.95" \ --weight_decay="0.1" \ --overwrite_output_dir \ --logging_steps="25" \ --eval_steps="500" \ --push_to_hub="False" \ --report_to="all" \ --dtype="bfloat16" \ --skip_memory_metrics="True" \ --save_steps="500" \ --save_total_limit 10 \ --gradient_accumulation_steps 16 \ --report_to="wandb" \ --run_name="gpt-neo-125M-code-clippy-dedup-filtered-no-resize-2048bs" \ --max_eval_samples 2000 \ --save_optimizer true ``` ## Intended Use and Limitations The model is finetuned text file from github repositories (mostly programming languages but also markdown and other project related files). ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup") tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-dedup") prompt = """def greet(name): '''A function to greet user. Given a user name it should say hello''' """ input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device) start = input_ids.size(1) out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, early_stopping=True, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0][start:])) ``` ### Limitations and Biases The model is intended to be used for research purposes and comes with no guarantees of quality of generated code. The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. As well as some differences in views from the paper, particularly around legal implications. 1. Over-reliance: This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model. 2. Economic and labor market impacts: Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [ONET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software. 3. Security implications:* No filtering or checking of vulnerabilities or buggy code was performed on the datase this model is trained on. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, this model may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, this model may be able to be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. Legal implications: No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any code generated with this model may be required to obey license terms that align with the software it was trained on such as GPL-3.0. It is unclear the legal ramifications of using a language model trained on this dataset. 5. Biases: The programming languages most represented in the dataset this model was trained on are Javascript and Python. Therefore, other, still popular languages such as C and C++, are less represented and therefore the models performance for these languages will be less comparatively. Additionally, this dataset only contains public repositories and so the model may not generate code that is representative of code written by private developers. No filtering was performed for potential racist, offensive, or otherwise inappropriate content. Therefore, this model may reflect such biases in its generation. GPT-Neo-125M-Code-Clippy-Dedup is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details. ## Eval results Coming soon...
flax-community/gpt-neo-125M-code-search-all	flax-community	2021-07-26T14:07:11Z	6	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# GPT-Code-Clippy-125M-Code-Search-All > Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot ## Model Description GPT-CC-125M-Code-Search is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on all languages in the [CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net). This model is specialized to autocomplete methods in multiple programming languages. ## Training data [CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net). ## Training procedure The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_flax.py). ```bash ./run_clm_flax.py \ --output_dir $HOME/gpt-neo-125M-code-search-all \ --model_name_or_path="EleutherAI/gpt-neo-125M" \ --dataset_name code_search_net \ --dataset_config_name="all" \ --do_train --do_eval \ --block_size="512" \ --per_device_train_batch_size="32" \ --per_device_eval_batch_size="64" \ --preprocessing_num_workers="8" \ --learning_rate="1.2e-4" \ --num_train_epochs 20 \ --warmup_steps 3000 \ --adam_beta1="0.9" \ --adam_beta2="0.95" \ --weight_decay="0.1" \ --overwrite_output_dir \ --logging_steps="25" \ --eval_steps="500" \ --push_to_hub="False" \ --report_to="all" \ --dtype="bfloat16" \ --skip_memory_metrics="True" \ --save_steps="500" \ --save_total_limit 10 \ --report_to="wandb" \ --run_name="gpt-neo-125M-code-search-all" ``` ## Intended Use and Limitations The model is finetuned methods from several languages and is intended to autocomplete methods given some prompt (method signature and docstring). ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-all") tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-all") prompt = """def greet(name): '''A function to greet user. Given a user name it should say hello''' """ input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device) start = input_ids.size(1) out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, early_stopping=True, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0][start:])) ``` ### Limitations and Biases The model is intended to be used for research purposes and comes with no guarantees of quality of generated code. GPT-CC is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details. ## Eval results Coming soon...
flax-community/gpt-neo-125M-code-search-py	flax-community	2021-07-26T14:06:51Z	6	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# GPT-Code-Clippy-125M-Code-Search-Py > Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot ## Model Description GPT-CC-125M-Code-Search is a [GPT-Neo-125M model](https://huggingface.co/EleutherAI/gpt-neo-125M) finetuned using causal language modeling on only the python language in the [CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net). This model is specialized to autocomplete methods in the python language. ## Training data [CodeSearchNet Challenge dataset](https://huggingface.co/datasets/code_search_net). ## Training procedure The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_flax.py). ```bash ./run_clm_flax.py \ --output_dir $HOME/gpt-neo-125M-code-search-py \ --model_name_or_path="EleutherAI/gpt-neo-125M" \ --dataset_name code_search_net \ --dataset_config_name="python" \ --do_train --do_eval \ --block_size="512" \ --per_device_train_batch_size="32" \ --per_device_eval_batch_size="64" \ --preprocessing_num_workers="8" \ --learning_rate="1.2e-4" \ --num_train_epochs 20 \ --warmup_steps 3000 \ --adam_beta1="0.9" \ --adam_beta2="0.95" \ --weight_decay="0.1" \ --overwrite_output_dir \ --logging_steps="25" \ --eval_steps="500" \ --push_to_hub="False" \ --report_to="all" \ --dtype="bfloat16" \ --skip_memory_metrics="True" \ --save_steps="500" \ --save_total_limit 10 \ --report_to="wandb" \ --run_name="gpt-neo-125M-code-search-py" ``` ## Intended Use and Limitations The model is finetuned methods from the python language and is intended to autocomplete python methods given some prompt (method signature and docstring). ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-py") tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-code-clippy-code-search-py") prompt = """def greet(name): '''A function to greet user. Given a user name it should say hello''' """ input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device) start = input_ids.size(1) out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, early_stopping=True, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0][start:])) ``` ### Limitations and Biases The model is intended to be used for research purposes and comes with no guarantees of quality of generated code. GPT-CC is finetuned from GPT-Neo and might have inherited biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details. ## Eval results Coming soon...
Alireza1044/albert-base-v2-rte	Alireza1044	2021-07-26T12:02:09Z	22	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model_index: - name: rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metric: name: Accuracy type: accuracy value: 0.6859205776173285 --- <!-- 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. --> # rte This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7994 - Accuracy: 0.6859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
Alireza1044/albert-base-v2-mrpc	Alireza1044	2021-07-26T11:46:48Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model_index: - name: mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metric: name: F1 type: f1 value: 0.901060070671378 --- <!-- 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. --> # mrpc This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4171 - Accuracy: 0.8627 - F1: 0.9011 - Combined Score: 0.8819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
Alireza1044/albert-base-v2-stsb	Alireza1044	2021-07-26T10:57:27Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model_index: - name: stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metric: name: Spearmanr type: spearmanr value: 0.9050744778895732 --- <!-- 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. --> # stsb This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.3978 - Pearson: 0.9090 - Spearmanr: 0.9051 - Combined Score: 0.9071 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 ### Training results ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
Maltehb/aelaectra-danish-electra-small-cased-ner-dane	Maltehb	2021-07-26T08:48:30Z	20	2	transformers	[ "transformers", "pytorch", "tf", "electra", "token-classification", "ælæctra", "danish", "ELECTRA-Small", "replaced token detection", "da", "dataset:DAGW", "arxiv:2003.10555", "arxiv:1810.04805", "arxiv:2005.03521", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- language: "da" tags: - ælæctra - pytorch - danish - ELECTRA-Small - replaced token detection license: "mit" datasets: - DAGW widget: - text: "Chili Jensen, som bor på Danmarksgade 12, køber chilifrugter fra Netto." metrics: - f1 --- # Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen. Ælæctra is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Ælæctra was pretrained with the ELECTRA-Small (Clark et al., 2020) pretraining approach by using the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020) and evaluated on Named Entity Recognition (NER) tasks. Since NER only presents a limited picture of Ælæctra's capabilities I am very interested in further evaluations. Therefore, if you employ it for any task, feel free to hit me up your findings! Ælæctra was, as mentioned, created to enhance the Danish NLP capabilties and please do note how this GitHub still does not support the Danish characters "Æ, Ø and Å" as the title of this repository becomes "-l-ctra". How ironic.🙂 Here is an example on how to load the finetuned Ælæctra-cased model for Named Entity Recognition in [PyTorch](https://pytorch.org/) using the [🤗Transformers](https://github.com/huggingface/transformers) library: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maltehb/-l-ctra-danish-electra-small-cased-ner-dane") model = AutoModelForTokenClassification.from_pretrained("Maltehb/-l-ctra-danish-electra-small-cased-ner-dane") ``` ### Evaluation of current Danish Language Models Ælæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated: \| Model \| Layers \| Hidden Size \| Params \| AVG NER micro-f1 (DaNE-testset) \| Average Inference Time (Sec/Epoch) \| Download \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Ælæctra Uncased \| 12 \| 256 \| 13.7M \| 78.03 (SD = 1.28) \| 10.91 \| [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) \| \| Ælæctra Cased \| 12 \| 256 \| 14.7M \| 80.08 (SD = 0.26) \| 10.92 \| [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) \| \| DaBERT \| 12 \| 768 \| 110M \| 84.89 (SD = 0.64) \| 43.03 \| [Link for model](https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1) \| \| mBERT Uncased \| 12 \| 768 \| 167M \| 80.44 (SD = 0.82) \| 72.10 \| [Link for model](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip) \| \| mBERT Cased \| 12 \| 768 \| 177M \| 83.79 (SD = 0.91) \| 70.56 \| [Link for model](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip) \| On [DaNE](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) without the MISC-tag, Ælæctra scores slightly worse than both cased and uncased Multilingual BERT (Devlin et al., 2019) and Danish BERT (Danish BERT, 2019/2020), however, Ælæctra is less than one third the size, and uses significantly fewer computational resources to pretrain and instantiate. ### Pretraining To pretrain Ælæctra it is recommended to build a Docker Container from the [Dockerfile](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/). Next, simply follow the [pretraining notebooks](https://github.com/MalteHB/Ælæctra/tree/master/infrastructure/Dockerfile/) The pretraining was done by utilizing a single NVIDIA Tesla V100 GPU with 16 GiB, endowed by the Danish data company [KMD](https://www.kmd.dk/). The pretraining took approximately 4 days and 9.5 hours for both the cased and uncased model ### Fine-tuning To fine-tune any Ælæctra model follow the [fine-tuning notebooks](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/) ### References Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ArXiv:2003.10555 [Cs]. http://arxiv.org/abs/2003.10555 Danish BERT. (2020). BotXO. https://github.com/botxo/nordic_bert (Original work published 2019) Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805 Hvingelby, R., Pauli, A. B., Barrett, M., Rosted, C., Lidegaard, L. M., & Søgaard, A. (2020). DaNE: A Named Entity Resource for Danish. Proceedings of the 12th Language Resources and Evaluation Conference, 4597–4604. https://www.aclweb.org/anthology/2020.lrec-1.565 Strømberg-Derczynski, L., Baglini, R., Christiansen, M. H., Ciosici, M. R., Dalsgaard, J. A., Fusaroli, R., Henrichsen, P. J., Hvingelby, R., Kirkedal, A., Kjeldsen, A. S., Ladefoged, C., Nielsen, F. Å., Petersen, M. L., Rystrøm, J. H., & Varab, D. (2020). The Danish Gigaword Project. ArXiv:2005.03521 [Cs]. http://arxiv.org/abs/2005.03521 #### Acknowledgements As the majority of this repository is build upon [the works](https://github.com/google-research/electra) by the team at Google who created ELECTRA, a HUGE thanks to them is in order. A Giga thanks also goes out to the incredible people who collected The Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020). Furthermore, I would like to thank my supervisor [Riccardo Fusaroli](https://github.com/fusaroli) for the support with the thesis, and a special thanks goes out to [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen) for his continuous feedback. Lastly, i would like to thank KMD, my colleagues from KMD, and my peers and co-students from Cognitive Science for encouriging me to keep on working hard and holding my head up high! #### Contact For help or further information feel free to connect with the author Malte Højmark-Bertelsen on [[email protected]](mailto:[email protected]?subject=[GitHub]%20ÆlæctraCasedNER) or any of the following platforms: [<img align="left" alt="MalteHB \| Twitter" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/twitter.svg" />][twitter] [<img align="left" alt="MalteHB \| LinkedIn" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/linkedin.svg" />][linkedin] [<img align="left" alt="MalteHB \| Instagram" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/instagram.svg" />][instagram] <br /> </details> [twitter]: https://twitter.com/malteH_B [instagram]: https://www.instagram.com/maltemusen/ [linkedin]: https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/
huggingtweets/goodtweet_man	huggingtweets	2021-07-26T06:09:24Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/goodtweet_man/1627279760723/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1347063976585293826/11EjcLnX_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Good Tweetman</div> <div style="text-align: center; font-size: 14px;">@goodtweet_man</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Good Tweetman. \| Data \| Good Tweetman \| \| --- \| --- \| \| Tweets downloaded \| 3225 \| \| Retweets \| 734 \| \| Short tweets \| 643 \| \| Tweets kept \| 1848 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2czt5qbq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goodtweet_man's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tanvki3u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tanvki3u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goodtweet_man') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ForutanRad/bert-fa-QA-v1	ForutanRad	2021-07-26T03:51:47Z	20	2	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "arxiv:2005.12515", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model_index: - name: bert-fa-QA-v1 results: - task: name: Question Answering type: question-answering --- <!-- 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. --> # bert-fa-QA-v1 Persian Question and answer Model Based on Bert Model This model is a fine-tuned version of [ParsBERT](https://arxiv.org/abs/2005.12515) on PersianQA dataset. It achieves the following results on the evaluation set: - Loss: 1.7297 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2563 \| 1.0 \| 1126 \| 1.7222 \| \| 1.3372 \| 2.0 \| 2252 \| 1.7297 \| ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
flax-sentence-embeddings/stackoverflow_mpnet-base	flax-sentence-embeddings	2021-07-26T01:36:33Z	5,238	5	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # stackoverflow_mpnet-base This is a microsoft/mpnet-base model trained on 18,562,443 (title, body) pairs from StackOverflow. SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) model and trained it using Siamese Network setup and contrastive learning objective. 18,562,443 (title, body) pairs from StackOverflow was used as training data. For this model, mean pooling of hidden states were used as sentence embeddings. See data_config.json and train_script.py in this respository how the model was trained and which datasets have been used. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/stackoverflow_mpnet-base') text = "Replace me by any question / answer you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We used 18,562,443 (title, body) pairs from StackOverflow as training data. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| StackOverflow title body pairs \| - \| 18,562,443 \|
flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot	flax-sentence-embeddings	2021-07-26T01:34:59Z	8	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "arxiv:2102.07033", "arxiv:2104.08727", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # multi-qa_v1-mpnet-cls_dot ## Model Description SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, cls output was used instead of mean pooling as sentence embeddings. Dot product was used to calculate similarity for learning objective. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot') text = "Replace me by any question / answer you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) \| - \| 4,750,619 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| SearchQA \| - \| 582,261 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \|
flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot	flax-sentence-embeddings	2021-07-26T01:33:36Z	354	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "arxiv:2102.07033", "arxiv:2104.08727", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # multi-qa_v1-MiniLM-L6-cls_dot ## Model Description SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used a pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and trained it using Siamese Network setup and contrastive learning objective. Question and answer pairs from StackExchange was used as training data to make the model robust to Question / Answer embedding similarity. For this model, cls output was used instead of mean pooling as sentence embeddings. Dot product was used to calculate similarity for learning objective. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence encoder for a search engine. Given an input sentence, it outputs a vector which captures the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot') text = "Replace me by any question / answer you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) \| - \| 4,750,619 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| SearchQA \| - \| 582,261 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \|
huggingtweets/imgrimevil	huggingtweets	2021-07-25T22:26:32Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/imgrimevil/1627251988335/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1397711387380617219/Hzreffrt_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Contra</div> <div style="text-align: center; font-size: 14px;">@imgrimevil</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Contra. \| Data \| Contra \| \| --- \| --- \| \| Tweets downloaded \| 3238 \| \| Retweets \| 669 \| \| Short tweets \| 582 \| \| Tweets kept \| 1987 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kn7qqp8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @imgrimevil's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fjaoumhd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fjaoumhd/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/imgrimevil') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/horse1350	huggingtweets	2021-07-25T22:17:33Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/horse1350/1627251450034/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417892518981738502/Qb2SoLGO_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">horsey</div> <div style="text-align: center; font-size: 14px;">@horse1350</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from horsey. \| Data \| horsey \| \| --- \| --- \| \| Tweets downloaded \| 1783 \| \| Retweets \| 26 \| \| Short tweets \| 352 \| \| Tweets kept \| 1405 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gpy0wuu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @horse1350's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fgwh8u1y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fgwh8u1y/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/horse1350') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/k_saifullaah	huggingtweets	2021-07-25T21:49:06Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/k_saifullaah/1627249742146/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1399131958072856576/kNZ_xofA_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Khalid Saifullah</div> <div style="text-align: center; font-size: 14px;">@k_saifullaah</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Khalid Saifullah. \| Data \| Khalid Saifullah \| \| --- \| --- \| \| Tweets downloaded \| 2418 \| \| Retweets \| 82 \| \| Short tweets \| 698 \| \| Tweets kept \| 1638 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wq0m2z1a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @k_saifullaah's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12hp7hqj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12hp7hqj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/k_saifullaah') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A	flax-sentence-embeddings	2021-07-25T21:33:06Z	10	3	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "arxiv:2102.07033", "arxiv:2104.08727", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # multi-QA_v1-mpnet-asymmetric-A ## Model Description SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used two separate pretrained [mpnet-base](https://huggingface.co/microsoft/mpnet-base) models and trained them using contrastive learning objective. Question and answer pairs from StackExchange and other datasets were used as training data to make the model robust to Question / Answer embedding similarity. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses This model set is intended to be used as a sentence encoder for a search engine. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks. Two models should be used on conjunction for Semantic Search purposes. 1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q) - Model to encode Questions 1. [multi-QA_v1-mpnet-asymmetric-A](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A) - Model to encode Answers ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q') model_A = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A') question = "Replace me by any question you'd like." question_embbedding = model_Q.encode(text) answer = "Replace me by any answer you'd like." answer_embbedding = model_A.encode(text) answer_likeliness = cosine_similarity(question_embedding, answer_embedding) ``` # Training procedure ## Pre-training We use the pretrained [`Mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) \| - \| 4,750,619 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| SearchQA \| - \| 582,261 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \|
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q	flax-sentence-embeddings	2021-07-25T21:32:52Z	7	1	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "arxiv:2102.07033", "arxiv:2104.08727", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # multi-QA_v1-mpnet-asymmetric-Q ## Model Description SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used two separate pretrained [mpnet-base](https://huggingface.co/microsoft/mpnet-base) models and trained them using contrastive learning objective. Question and answer pairs from StackExchange and other datasets were used as training data to make the model robust to Question / Answer embedding similarity. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses This model set is intended to be used as a sentence encoder for a search engine. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks. Two models should be used on conjunction for Semantic Search purposes. 1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q) - Model to encode Questions 1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A) - Model to encode Answers ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q') model_A = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A') question = "Replace me by any question you'd like." question_embbedding = model_Q.encode(text) answer = "Replace me by any answer you'd like." answer_embbedding = model_A.encode(text) answer_likeliness = cosine_similarity(question_embedding, answer_embedding) ``` # Training procedure ## Pre-training We use the pretrained [`Mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) \| - \| 4,750,619 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| SearchQA \| - \| 582,261 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \|
huggingtweets/aimbotaimy-ladydarknest	huggingtweets	2021-07-25T20:33:04Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/aimbotaimy-ladydarknest/1627245180529/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374872808136835072/hPahIg-A_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409725677495009283/RPVDIGan_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞</div> <div style="text-align: center; font-size: 14px;">@aimbotaimy-ladydarknest</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from AimbotAimy 🍞🔞 NSFW V-Tuber & Demon Lord Yeefi NSFW🔞. \| Data \| AimbotAimy 🍞🔞 NSFW V-Tuber \| Demon Lord Yeefi NSFW🔞 \| \| --- \| --- \| --- \| \| Tweets downloaded \| 528 \| 3242 \| \| Retweets \| 61 \| 957 \| \| Short tweets \| 130 \| 392 \| \| Tweets kept \| 337 \| 1893 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uz56dprc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aimbotaimy-ladydarknest's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1di7czlx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/aimbotaimy-ladydarknest') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tugstugi/wav2vec2-large-xlsr-53-kalmyk	tugstugi	2021-07-25T19:55:31Z	13	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "speech", "audio", "xal", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: xal tags: - speech - audio - automatic-speech-recognition license: apache-2.0 --- ## Info This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp#datasets) created by a voice conversion model. * 50% WER on a private test set created from Kalmyk TV recordnings * on clean voice recordings, the model should have much lower WER * voice conversion info * 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp#datasets) * The source voice is a Kalmyk female voice TTS * Target voices are from the VCTK dataset * example data: https://twitter.com/tugstugi/status/1409111296897912835 * each WAV has a different text created from Kalmyk books
huggingtweets/colinb_pdx	huggingtweets	2021-07-25T18:19:32Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/colinb_pdx/1627237168140/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1392748308280406020/XckpJcJ8_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Colin Bisson</div> <div style="text-align: center; font-size: 14px;">@colinb_pdx</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Colin Bisson. \| Data \| Colin Bisson \| \| --- \| --- \| \| Tweets downloaded \| 2057 \| \| Retweets \| 161 \| \| Short tweets \| 90 \| \| Tweets kept \| 1806 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vpxju9g9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @colinb_pdx's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/epdq8lc0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/epdq8lc0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/colinb_pdx') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/aimbotaimy-demi_naga-livingscribe	huggingtweets	2021-07-25T18:00:56Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/aimbotaimy-demi_naga-livingscribe/1627235967135/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374872808136835072/hPahIg-A_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1405364006475296773/0i4RCEH5_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408966863804063749/fTuaNcZ__400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga</div> <div style="text-align: center; font-size: 14px;">@aimbotaimy-demi_naga-livingscribe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from AimbotAimy 🍞🔞 NSFW V-Tuber & Poe's Law 🇷🇺: 3.33 You can (not) redo & Demi 'ドヤ顔' Naga. \| Data \| AimbotAimy 🍞🔞 NSFW V-Tuber \| Poe's Law 🇷🇺: 3.33 You can (not) redo \| Demi 'ドヤ顔' Naga \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 497 \| 3242 \| 3234 \| \| Retweets \| 60 \| 433 \| 909 \| \| Short tweets \| 125 \| 564 \| 341 \| \| Tweets kept \| 312 \| 2245 \| 1984 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32v27r5o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aimbotaimy-demi_naga-livingscribe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qs4c0sr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/aimbotaimy-demi_naga-livingscribe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/jackbutcher-paikcapital-thedankoe	huggingtweets	2021-07-25T16:41:39Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1251200537388695557/96JxUIrJ_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1384243878748856321/vreel6UH_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417910390051246080/wKq6pjPR_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">DAN KOE & humble farmer & Jack Butcher</div> <div style="text-align: center; font-size: 14px;">@jackbutcher-paikcapital-thedankoe</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from DAN KOE & humble farmer & Jack Butcher. \| Data \| DAN KOE \| humble farmer \| Jack Butcher \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3249 \| 3247 \| 3220 \| \| Retweets \| 18 \| 601 \| 208 \| \| Short tweets \| 899 \| 500 \| 1048 \| \| Tweets kept \| 2332 \| 2146 \| 1964 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mvqun4ol/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jackbutcher-paikcapital-thedankoe's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qd8720q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qd8720q/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/jackbutcher-paikcapital-thedankoe') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Alireza1044/albert-base-v2-cola	Alireza1044	2021-07-25T16:25:10Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model_index: - name: cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metric: name: Matthews Correlation type: matthews_correlation value: 0.5494768667363472 --- <!-- 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. --> # cola This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.7552 - Matthews Correlation: 0.5495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 4.0 ### Training results ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
flax-community/roberta-swahili	flax-community	2021-07-25T16:21:02Z	5	2	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "sw", "dataset:flax-community/swahili-safi", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: sw widget: - text: "Si kila mwenye makucha <mask> simba." datasets: - flax-community/swahili-safi --- ## RoBERTa in Swahili This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team. ## How to use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("flax-community/roberta-swahili") model = AutoModelForMaskedLM.from_pretrained("flax-community/roberta-swahili") print(round((model.num_parameters())/(10001000)),"Million Parameters") 105 Million Parameters ``` #### Training Data: This model was trained on [Swahili Safi](https://huggingface.co/datasets/flax-community/swahili-safi) #### Results: [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1OIurb4J91X7461NQXLCCGzjeEGJq_Tyl?usp=sharing) ``` Eval metrics: {'f1': 86%} ``` This [model](https://huggingface.co/flax-community/roberta-swahili-news-classification) was fine-tuned based off this model for the [Zindi News Classification Challenge](https://zindi.africa/hackathons/ai4d-swahili-news-classification-challenge) #### More Details*: For more details and Demo please check [HF Swahili Space](https://huggingface.co/spaces/flax-community/Swahili)
vivekRahul/animal_classifier_huggingface	vivekRahul	2021-07-25T06:02:38Z	88	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: animal_classifier_huggingface results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9910714030265808 --- # animal_classifier_huggingface Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg) #### elephant ![elephant](images/elephant.jpg) #### lion ![lion](images/lion.jpg) #### tiger ![tiger](images/tiger.jpg)
huggingtweets/_nisagiss-dril	huggingtweets	2021-07-25T04:05:01Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/_nisagiss-dril/1627185897572/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1320596112676409344/rgbeQhIA_400x400.png')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nisa 🇲🇽 & wint</div> <div style="text-align: center; font-size: 14px;">@_nisagiss-dril</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nisa 🇲🇽 & wint. \| Data \| Nisa 🇲🇽 \| wint \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3053 \| 3229 \| \| Retweets \| 2657 \| 464 \| \| Short tweets \| 138 \| 311 \| \| Tweets kept \| 258 \| 2454 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/op7x5wkb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_nisagiss-dril's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1x66ooaf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1x66ooaf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/_nisagiss-dril') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/celosia2	huggingtweets	2021-07-24T17:58:19Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/celosia2/1627149452177/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1251490479990022145/lS6i5Wgy_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Celosia2 🌻 Kristi 💚</div> <div style="text-align: center; font-size: 14px;">@celosia2</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Celosia2 🌻 Kristi 💚. \| Data \| Celosia2 🌻 Kristi 💚 \| \| --- \| --- \| \| Tweets downloaded \| 3247 \| \| Retweets \| 613 \| \| Short tweets \| 494 \| \| Tweets kept \| 2140 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ohtfdalm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @celosia2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xzr0nuzp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xzr0nuzp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/celosia2') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/sshakestation	huggingtweets	2021-07-24T17:44:37Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/sshakestation/1627148673612/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1390378853877510145/YdbZXqjN_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">RJ's Shake Station</div> <div style="text-align: center; font-size: 14px;">@sshakestation</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from RJ's Shake Station. \| Data \| RJ's Shake Station \| \| --- \| --- \| \| Tweets downloaded \| 456 \| \| Retweets \| 10 \| \| Short tweets \| 28 \| \| Tweets kept \| 418 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wszsjtre/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sshakestation's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3k91nzds) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3k91nzds/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sshakestation') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/lauradmcbryde	huggingtweets	2021-07-24T15:03:09Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/lauradmcbryde/1627138961068/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1384965601492353026/KlIO_YsH_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Laura D McBryde</div> <div style="text-align: center; font-size: 14px;">@lauradmcbryde</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Laura D McBryde. \| Data \| Laura D McBryde \| \| --- \| --- \| \| Tweets downloaded \| 3233 \| \| Retweets \| 205 \| \| Short tweets \| 453 \| \| Tweets kept \| 2575 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ry0eljz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lauradmcbryde's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g2wyxs4u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g2wyxs4u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/lauradmcbryde') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/poss_em	huggingtweets	2021-07-24T05:28:10Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1416065098745999364/LaFosSZA_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Poss'em</div> <div style="text-align: center; font-size: 14px;">@poss_em</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Poss'em. \| Data \| Poss'em \| \| --- \| --- \| \| Tweets downloaded \| 245 \| \| Retweets \| 26 \| \| Short tweets \| 19 \| \| Tweets kept \| 200 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fn04icv3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @poss_em's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1m889m52) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1m889m52/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/poss_em') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/realbenfishbein	huggingtweets	2021-07-24T05:27:00Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1349511600974278662/7v0yTYob_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ben Fishbein</div> <div style="text-align: center; font-size: 14px;">@realbenfishbein</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ben Fishbein. \| Data \| Ben Fishbein \| \| --- \| --- \| \| Tweets downloaded \| 261 \| \| Retweets \| 8 \| \| Short tweets \| 30 \| \| Tweets kept \| 223 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2idreqex/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @realbenfishbein's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3me55h26) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3me55h26/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/realbenfishbein') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/jontthomas	huggingtweets	2021-07-24T02:44:18Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/jontthomas/1627094654118/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418758009673723909/rtsbsqKv_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Johnathon Taylor "JTT" Thomas</div> <div style="text-align: center; font-size: 14px;">@jontthomas</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Johnathon Taylor "JTT" Thomas. \| Data \| Johnathon Taylor "JTT" Thomas \| \| --- \| --- \| \| Tweets downloaded \| 239 \| \| Retweets \| 3 \| \| Short tweets \| 44 \| \| Tweets kept \| 192 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9haxw34n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jontthomas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bzdo7d6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bzdo7d6/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/jontthomas') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/c0up	huggingtweets	2021-07-24T01:26:20Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/c0up/1627089976491/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1209626102/c0up_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Abhilash</div> <div style="text-align: center; font-size: 14px;">@c0up</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Abhilash. \| Data \| Abhilash \| \| --- \| --- \| \| Tweets downloaded \| 3203 \| \| Retweets \| 1476 \| \| Short tweets \| 384 \| \| Tweets kept \| 1343 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1le73jjg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @c0up's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tebog4r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tebog4r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/c0up') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/staidindoors	huggingtweets	2021-07-23T23:26:09Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/staidindoors/1627082764759/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418465930456092672/-iGnfQyn_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">staid</div> <div style="text-align: center; font-size: 14px;">@staidindoors</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from staid. \| Data \| staid \| \| --- \| --- \| \| Tweets downloaded \| 3240 \| \| Retweets \| 919 \| \| Short tweets \| 611 \| \| Tweets kept \| 1710 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1crkj9xo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @staidindoors's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/it5qlwh5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/it5qlwh5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/staidindoors') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/smolserabean	huggingtweets	2021-07-23T22:52:13Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/smolserabean/1627080715021/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406727363522666497/86n4KIIJ_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ari but awesome</div> <div style="text-align: center; font-size: 14px;">@smolserabean</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ari but awesome. \| Data \| Ari but awesome \| \| --- \| --- \| \| Tweets downloaded \| 398 \| \| Retweets \| 150 \| \| Short tweets \| 70 \| \| Tweets kept \| 178 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tas8okv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @smolserabean's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3afn50i3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3afn50i3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/smolserabean') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/claireredacted-deepleffen	huggingtweets	2021-07-23T22:49:42Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/claireredacted-deepleffen/1627080578772/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/984455379659575296/-0punyb9_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241879678455078914/e2EdZIrr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Claire & Deep Leffen Bot</div> <div style="text-align: center; font-size: 14px;">@claireredacted-deepleffen</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Claire & Deep Leffen Bot. \| Data \| Claire \| Deep Leffen Bot \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3241 \| 493 \| \| Retweets \| 523 \| 13 \| \| Short tweets \| 627 \| 26 \| \| Tweets kept \| 2091 \| 454 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uxfbhyv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @claireredacted-deepleffen's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rdhjvg7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rdhjvg7/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/claireredacted-deepleffen') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/clamtime-daramgaria-ledgeguard	huggingtweets	2021-07-23T22:19:47Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409230363906424832/67a8m2BA_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408716131867713538/rg3HSZ5D_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415805087868391427/r5M55HF9_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ho3K \| Daramgar 🔜 CROSSxUP & clementine!!!! 𓃠 & camera! (low tier)</div> <div style="text-align: center; font-size: 14px;">@clamtime-daramgaria-ledgeguard</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ho3K \| Daramgar 🔜 CROSSxUP & clementine!!!! 𓃠 & camera! (low tier). \| Data \| Ho3K \| Daramgar 🔜 CROSSxUP \| clementine!!!! 𓃠 \| camera! (low tier) \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3249 \| 3185 \| 3211 \| \| Retweets \| 30 \| 439 \| 1053 \| \| Short tweets \| 807 \| 719 \| 556 \| \| Tweets kept \| 2412 \| 2027 \| 1602 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2z4hkysf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clamtime-daramgaria-ledgeguard's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2viwbf33) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2viwbf33/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/clamtime-daramgaria-ledgeguard') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/jimlbsp	huggingtweets	2021-07-23T22:17:03Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1418336753170239493/xADOlUfY_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Jimmy</div> <div style="text-align: center; font-size: 14px;">@jimlbsp</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Jimmy. \| Data \| Jimmy \| \| --- \| --- \| \| Tweets downloaded \| 3239 \| \| Retweets \| 348 \| \| Short tweets \| 383 \| \| Tweets kept \| 2508 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/213jvoqo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jimlbsp's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rtm7jla/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/jimlbsp') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/daramgaria	huggingtweets	2021-07-23T22:04:10Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1409230363906424832/67a8m2BA_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ho3K \| Daramgar 🔜 CROSSxUP</div> <div style="text-align: center; font-size: 14px;">@daramgaria</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ho3K \| Daramgar 🔜 CROSSxUP. \| Data \| Ho3K \| Daramgar 🔜 CROSSxUP \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 30 \| \| Short tweets \| 807 \| \| Tweets kept \| 2412 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z2deo4d4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @daramgaria's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29cuxcz9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29cuxcz9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/daramgaria') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/spamemcspam	huggingtweets	2021-07-23T20:59:12Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/spamemcspam/1627073948338/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362892272342196224/RSTBJB08_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Yes, I know my cat is ugly.</div> <div style="text-align: center; font-size: 14px;">@spamemcspam</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Yes, I know my cat is ugly.. \| Data \| Yes, I know my cat is ugly. \| \| --- \| --- \| \| Tweets downloaded \| 3214 \| \| Retweets \| 977 \| \| Short tweets \| 228 \| \| Tweets kept \| 2009 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mn5cki9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @spamemcspam's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1v7cmihj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1v7cmihj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/spamemcspam') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/cphilipzarina	huggingtweets	2021-07-23T18:08:49Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/cphilipzarina/1627063725221/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1049362687216562176/fLWP67_f_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">C Philip Zarina</div> <div style="text-align: center; font-size: 14px;">@cphilipzarina</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from C Philip Zarina. \| Data \| C Philip Zarina \| \| --- \| --- \| \| Tweets downloaded \| 71 \| \| Retweets \| 5 \| \| Short tweets \| 6 \| \| Tweets kept \| 60 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2500hnbe/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cphilipzarina's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/11qav433) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/11qav433/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cphilipzarina') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/elizamuffins	huggingtweets	2021-07-23T18:02:59Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/elizamuffins/1627063374286/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/819298508545126401/KR63pu1p_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Junior Movie Buff</div> <div style="text-align: center; font-size: 14px;">@elizamuffins</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Junior Movie Buff. \| Data \| Junior Movie Buff \| \| --- \| --- \| \| Tweets downloaded \| 3225 \| \| Retweets \| 290 \| \| Short tweets \| 295 \| \| Tweets kept \| 2640 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3jfflcwa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elizamuffins's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bixjnvi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bixjnvi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elizamuffins') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/perry_ruh	huggingtweets	2021-07-23T17:42:36Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/perry_ruh/1627062108629/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1370530189160091648/cbuh1tgC_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Perry R 💀</div> <div style="text-align: center; font-size: 14px;">@perry_ruh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Perry R 💀. \| Data \| Perry R 💀 \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 436 \| \| Short tweets \| 535 \| \| Tweets kept \| 2273 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tg0srg29/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @perry_ruh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/201o3y3w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/201o3y3w/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/perry_ruh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/lana_ray_dale	huggingtweets	2021-07-23T17:36:53Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/lana_ray_dale/1627061772839/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/439125466340143105/TZaoVrUl_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">R A Y</div> <div style="text-align: center; font-size: 14px;">@lana_ray_dale</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from R A Y. \| Data \| R A Y \| \| --- \| --- \| \| Tweets downloaded \| 718 \| \| Retweets \| 56 \| \| Short tweets \| 90 \| \| Tweets kept \| 572 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37ffw07m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lana_ray_dale's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uxn80y7g) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uxn80y7g/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/lana_ray_dale') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/herialc	huggingtweets	2021-07-23T17:32:09Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1233052680190296064/zcbLKhOR_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Claire</div> <div style="text-align: center; font-size: 14px;">@herialc</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Claire. \| Data \| Claire \| \| --- \| --- \| \| Tweets downloaded \| 539 \| \| Retweets \| 219 \| \| Short tweets \| 27 \| \| Tweets kept \| 293 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bop9va7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @herialc's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10twdkn3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10twdkn3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/herialc') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/john_tub_ocf	huggingtweets	2021-07-23T17:08:57Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/john_tub_ocf/1627060133532/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1414408906315546629/hyBAS2Pl_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">John Tub</div> <div style="text-align: center; font-size: 14px;">@john_tub_ocf</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from John Tub. \| Data \| John Tub \| \| --- \| --- \| \| Tweets downloaded \| 533 \| \| Retweets \| 90 \| \| Short tweets \| 88 \| \| Tweets kept \| 355 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kmh9ydg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @john_tub_ocf's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ondjsiu1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ondjsiu1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/john_tub_ocf') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/moviefishy	huggingtweets	2021-07-23T16:51:17Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/moviefishy/1627059072751/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408154042698665985/1PWi4RhY_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Rock Genius Fishy - Rock House Head</div> <div style="text-align: center; font-size: 14px;">@moviefishy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Rock Genius Fishy - Rock House Head. \| Data \| Rock Genius Fishy - Rock House Head \| \| --- \| --- \| \| Tweets downloaded \| 3238 \| \| Retweets \| 485 \| \| Short tweets \| 546 \| \| Tweets kept \| 2207 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/f99exm0b/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @moviefishy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3v5cszr1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3v5cszr1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/moviefishy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/justingaynor	huggingtweets	2021-07-23T16:38:16Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/justingaynor/1627058292596/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1181640506440392704/Gyr5t3Kt_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Justin Gaynor</div> <div style="text-align: center; font-size: 14px;">@justingaynor</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Justin Gaynor. \| Data \| Justin Gaynor \| \| --- \| --- \| \| Tweets downloaded \| 3237 \| \| Retweets \| 367 \| \| Short tweets \| 605 \| \| Tweets kept \| 2265 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vw2weob/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @justingaynor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/80n0rrtz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/80n0rrtz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/justingaynor') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/islamphobiacow-praisegodbarbon	huggingtweets	2021-07-23T16:06:26Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/islamphobiacow-praisegodbarbon/1627056382131/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1381764452098437120/74IgKP07_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1368077075127603200/Z08slO2P_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Boston Psychology PhD & keyvan</div> <div style="text-align: center; font-size: 14px;">@islamphobiacow-praisegodbarbon</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Boston Psychology PhD & keyvan. \| Data \| Boston Psychology PhD \| keyvan \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3224 \| 3242 \| \| Retweets \| 858 \| 179 \| \| Short tweets \| 251 \| 223 \| \| Tweets kept \| 2115 \| 2840 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3egvdux4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @islamphobiacow-praisegodbarbon's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/islamphobiacow-praisegodbarbon') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
flax-sentence-embeddings/all_datasets_v4_mpnet-base	flax-sentence-embeddings	2021-07-23T15:55:37Z	563	6	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "en", "arxiv:2104.08727", "arxiv:1810.09305", "arxiv:2102.07033", "arxiv:1904.06472", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en --- # Model description The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained ['mpnet-base'](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_mpnet-base') text = "Replace me by any text you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained ['mpnet-base'](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [COCO 2020](COCO 2020) \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [SPECTER](https://github.com/allenai/specter) \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| SearchQA \| - \| 582,261 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Question \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| total \| \| 1,097,953,922 \|
flax-sentence-embeddings/all_datasets_v3_MiniLM-L6	flax-sentence-embeddings	2021-07-23T15:53:06Z	121	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "en", "arxiv:2104.08727", "arxiv:1810.09305", "arxiv:2102.07033", "arxiv:1904.06472", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en --- # Model description The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L6') text = "Replace me by any text you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) which is a 6 layer version of ['microsoft/MiniLM-L12-H384-uncased'](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) by keeping only every second layer. Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [COCO 2020](COCO 2020) \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [SPECTER](https://github.com/allenai/specter) \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| SearchQA \| - \| 582,261 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Question \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| total \| \| 1,097,953,922 \|
flax-sentence-embeddings/all_datasets_v4_MiniLM-L6	flax-sentence-embeddings	2021-07-23T15:49:28Z	27,755	34	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "en", "arxiv:2104.08727", "arxiv:1810.09305", "arxiv:2102.07033", "arxiv:1904.06472", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en --- # Model description The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6') text = "Replace me by any text you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) which is a 6 layer version of ['microsoft/MiniLM-L12-H384-uncased'](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) by keeping only every second layer. Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [COCO 2020](COCO 2020) \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [SPECTER](https://github.com/allenai/specter) \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| SearchQA \| - \| 582,261 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Question \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| total \| \| 1,097,953,922 \|
flax-sentence-embeddings/all_datasets_v3_roberta-large	flax-sentence-embeddings	2021-07-23T15:45:17Z	5,030	13	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "en", "arxiv:2104.08727", "arxiv:1810.09305", "arxiv:2102.07033", "arxiv:1904.06472", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en --- # Model description The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`roberta-large`](https://huggingface.co/roberta-large) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_roberta-large') text = "Replace me by any text you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained [`roberta-large`](https://huggingface.co/roberta-large). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [COCO 2020](COCO 2020) \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [SPECTER](https://github.com/allenai/specter) \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| SearchQA \| - \| 582,261 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Question \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| total \| \| 1,097,953,922 \|
flax-sentence-embeddings/all_datasets_v3_MiniLM-L12	flax-sentence-embeddings	2021-07-23T15:37:42Z	435	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "en", "arxiv:2104.08727", "arxiv:1810.09305", "arxiv:2102.07033", "arxiv:1904.06472", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en --- # Model description The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`MiniLM-L12`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. ## How to use Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L12') text = "Replace me by any text you'd like." text_embbedding = model.encode(text) # array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106, # -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...], # dtype=float32) ``` # Training procedure ## Pre-training We use the pretrained [`MiniLM-L12`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased). Please refer to the model card for more detailed information about the pre-training procedure. ## Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. ### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository. ### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|:--------------------------------------------------------:\|:----------------------------------------:\|:--------------------------:\| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) \| - \| 364,001 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [COCO 2020](COCO 2020) \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [SPECTER](https://github.com/allenai/specter) \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [PAQ](https://github.com/facebookresearch/PAQ) \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| SearchQA \| - \| 582,261 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Title/Question \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| total \| \| 1,097,953,922 \|
huggingtweets/gozusabu	huggingtweets	2021-07-23T15:36:01Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/gozusabu/1627054557412/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1382600435056394242/azQoqzIb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Calum Macleod</div> <div style="text-align: center; font-size: 14px;">@gozusabu</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Calum Macleod. \| Data \| Calum Macleod \| \| --- \| --- \| \| Tweets downloaded \| 1926 \| \| Retweets \| 673 \| \| Short tweets \| 279 \| \| Tweets kept \| 974 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y71yp06o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gozusabu's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/dwp3t07q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/dwp3t07q/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gozusabu') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/timthom_007	huggingtweets	2021-07-23T15:30:29Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/timthom_007/1627054225472/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406641405150253059/RNJ6uGeN_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">TimThom 🍝</div> <div style="text-align: center; font-size: 14px;">@timthom_007</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from TimThom 🍝. \| Data \| TimThom 🍝 \| \| --- \| --- \| \| Tweets downloaded \| 1187 \| \| Retweets \| 89 \| \| Short tweets \| 225 \| \| Tweets kept \| 873 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37fjihoh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @timthom_007's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1tq742cw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1tq742cw/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/timthom_007') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/nebaris	huggingtweets	2021-07-23T15:24:04Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/nebaris/1627053702291/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1411066012049588224/HL_0eL2p_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Internet🎋Katy</div> <div style="text-align: center; font-size: 14px;">@nebaris</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Internet🎋Katy. \| Data \| Internet🎋Katy \| \| --- \| --- \| \| Tweets downloaded \| 3242 \| \| Retweets \| 297 \| \| Short tweets \| 707 \| \| Tweets kept \| 2238 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vll1xzfk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nebaris's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29pso84z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29pso84z/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nebaris') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
gwynethfae/t5-small-finetuned-xsum	gwynethfae	2021-07-23T15:08:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model_index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation --- <!-- 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. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 13 \| 3.6429 \| 15.3135 \| 1.0725 \| 12.0447 \| 12.445 \| 18.97 \| ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
danlou/aristo-roberta-finetuned-csqa	danlou	2021-07-23T14:33:00Z	6	1	transformers	[ "transformers", "pytorch", "roberta", "multiple-choice", "generated_from_trainer", "dataset:commonsense_qa", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - commonsense_qa metrics: - accuracy model_index: - name: aristo-roberta-finetuned-csqa results: - dataset: name: commonsense_qa type: commonsense_qa args: default metric: name: Accuracy type: accuracy value: 0.7305487394332886 --- <!-- 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. --> # aristo-roberta-finetuned-csqa This model is a fine-tuned version of [LIAMF-USP/aristo-roberta](https://huggingface.co/LIAMF-USP/aristo-roberta) on the commonsense_qa dataset. It achieves the following results on the evaluation set: - Loss: 1.2187 - Accuracy: 0.7305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.131 \| 1.0 \| 609 \| 0.7109 \| 0.7232 \| \| 0.6957 \| 2.0 \| 1218 \| 0.6912 \| 0.7346 \| \| 0.459 \| 3.0 \| 1827 \| 0.8364 \| 0.7305 \| \| 0.3063 \| 4.0 \| 2436 \| 1.0595 \| 0.7322 \| \| 0.2283 \| 5.0 \| 3045 \| 1.2187 \| 0.7305 \| ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0 - Datasets 1.10.2 - Tokenizers 0.10.3
huggingtweets/mjrotoni	huggingtweets	2021-07-23T13:27:50Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/mjrotoni/1627046866828/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1397512749316337664/Tb-2O_z7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">marcvs🦑🍃📸🖊️💜</div> <div style="text-align: center; font-size: 14px;">@mjrotoni</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from marcvs🦑🍃📸🖊️💜. \| Data \| marcvs🦑🍃📸🖊️💜 \| \| --- \| --- \| \| Tweets downloaded \| 3151 \| \| Retweets \| 774 \| \| Short tweets \| 605 \| \| Tweets kept \| 1772 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/abanc5lt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mjrotoni's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tzpbf9g) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tzpbf9g/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mjrotoni') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Pyjay/bert-base-dutch-cased-finetuned-gv	Pyjay	2021-07-23T08:54:10Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model_index: - name: bert-base-dutch-cased-finetuned-gv results: - task: name: Masked Language Modeling type: fill-mask --- <!-- 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. --> # bert-base-dutch-cased-finetuned-gv This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 1.7837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4741 \| 1.0 \| 2603 \| 1.8404 \| \| 1.2384 \| 2.0 \| 5206 \| 1.8457 \| \| 1.2121 \| 3.0 \| 7809 \| 1.7837 \| ### Framework versions - Transformers 4.9.0 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
TransQuest/siamesetransquest-da-et_en-wiki	TransQuest	2021-07-23T08:31:12Z	38	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "Quality Estimation", "siamesetransquest", "da", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	--- language: et-en tags: - Quality Estimation - siamesetransquest - da license: apache-2.0 --- # TransQuest: Translation Quality Estimation with Cross-lingual Transformers The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level. With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest). ## Features - Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment. - Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps. - Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented. - Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest) ## Installation ### From pip ```bash pip install transquest ``` ### From Source ```bash git clone https://github.com/TharinduDR/TransQuest.git cd TransQuest pip install -r requirements.txt ``` ## Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-et_en-wiki") predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ``` ## Documentation For more details follow the documentation. 1. [Installation](https://tharindudr.github.io/TransQuest/install/) - Install TransQuest locally using pip. 2. Architectures - Checkout the architectures implemented in TransQuest 1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation. 2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation. 3. Examples - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks. 1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/) 2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/) 4. Pre-trained Models - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level 1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/) 2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/) 5. [Contact](https://tharindudr.github.io/TransQuest/contact/) - Contact us for any issues with TransQuest ## Citations If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/). ```bash @InProceedings{ranasinghe2021, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics}, year = {2021} } ``` If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020. ```bash @InProceedings{transquest:2020a, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, year = {2020} } ``` ```bash @InProceedings{transquest:2020b, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest at WMT2020: Sentence-Level Direct Assessment}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, year = {2020} } ```
ehdwns1516/gpt3-kor-based_gpt2_review_SR4	ehdwns1516	2021-07-23T01:18:45Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# ehdwns1516/gpt3-kor-based_gpt2_review_SR4 * This model has been trained Korean dataset as a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5) ## Overview Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2) Language: Korean Training data: review_body dataset with a star of 4 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR4") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR4") generator = pipeline( "text-generation", model="ehdwns1516/gpt3-kor-based_gpt2_review_SR4", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
ehdwns1516/gpt3-kor-based_gpt2_review_SR3	ehdwns1516	2021-07-23T01:18:13Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# ehdwns1516/gpt3-kor-based_gpt2_review_SR3 * This model has been trained Korean dataset as a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5) ## Overview Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2) Language: Korean Training data: review_body dataset with a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3") generator = pipeline( "text-generation", model="ehdwns1516/gpt3-kor-based_gpt2_review_SR3", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
ehdwns1516/gpt3-kor-based_gpt2_review_SR1	ehdwns1516	2021-07-23T01:17:45Z	12	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# ehdwns1516/gpt3-kor-based_gpt2_review_SR1 * This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4) * [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5) ## Overview Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2) Language: Korean Training data: review_body dataset with a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1") generator = pipeline( "text-generation", model="ehdwns1516/gpt3-kor-based_gpt2_review_SR1", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
ehdwns1516/gpt2_review_star4	ehdwns1516	2021-07-23T01:07:26Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# gpt2_review_star4 * This model has been trained as a review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1) * [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2) * [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3) * [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4) * [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5) ## Overview Language model: [gpt2](https://huggingface.co/gpt2) Language: English Training data: review_body dataset with a star of 4 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star3") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star3") generator = pipeline( "text-generation", model="ehdwns1516/gpt2_review_star4", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
ehdwns1516/gpt2_review_star2	ehdwns1516	2021-07-23T01:06:41Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# gpt2_review_star2 * This model has been trained as a review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1) * [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2) * [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3) * [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4) * [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5) ## Overview Language model: [gpt2](https://huggingface.co/gpt2) Language: English Training data: review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star2") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star2") generator = pipeline( "text-generation", model="ehdwns1516/gpt2_review_star2", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
ehdwns1516/gpt2_review_star1	ehdwns1516	2021-07-23T01:06:16Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# gpt2_review_star1 * This model has been trained as a review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). * Input text what you want to generate review. * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well. review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/) review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator) ## Model links for each 1 to 5 star * [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1) * [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2) * [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3) * [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4) * [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5) ## Overview Language model: [gpt2](https://huggingface.co/gpt2) Language: English Training data: review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi). Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note) ## Usage ## In Transformers ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star1") model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star1") generator = pipeline( "text-generation", model="ehdwns1516/gpt2_review_star1", tokenizer=tokenizer ) context = "your context" result = dict() result[0] = generator(context)[0] ```
shahrukhx01/distilbart-cnn-12-6-text2sql	shahrukhx01	2021-07-22T08:38:17Z	9	2	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	The distilbart-cnn-12-6-text2sql is fine-tuned on WIKISQL dataset. ```python from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig model = BartForConditionalGeneration.from_pretrained('shahrukhx01/distilbart-cnn-12-6-text2sql') tokenizer = BartTokenizer.from_pretrained('shahrukhx01/distilbart-cnn-12-6-text2sql') TEXT_QUERY = "what is the temperature of berlin " inputs = tokenizer([TEXT_QUERY], max_length=1024, return_tensors='pt') # Generate SQL text_query_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True) print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in text_query_ids]) ```
tylerroofingcompany/newwebsite	tylerroofingcompany	2021-07-22T06:45:54Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	Hugging Face's logo Hugging Face Search models, datasets, users... Models Datasets Resources Solutions Pricing Roofing Company In Tyler Tx's picture tylerroofingcompany / newwebsite Copied Model card Files and versions Settings newwebsite / .gitattributes system initial commit 3e5f8b4 7 seconds ago raw history blame edit 737 Bytes .bin. filter=lfs diff=lfs merge=lfs -text .lfs. filter=lfs diff=lfs merge=lfs -text .bin filter=lfs diff=lfs merge=lfs -text .h5 filter=lfs diff=lfs merge=lfs -text .tflite filter=lfs diff=lfs merge=lfs -text .tar.gz filter=lfs diff=lfs merge=lfs -text .ot filter=lfs diff=lfs merge=lfs -text .onnx filter=lfs diff=lfs merge=lfs -text .arrow filter=lfs diff=lfs merge=lfs -text .ftz filter=lfs diff=lfs merge=lfs -text .joblib filter=lfs diff=lfs merge=lfs -text .model filter=lfs diff=lfs merge=lfs -text .msgpack filter=lfs diff=lfs merge=lfs -text .pb filter=lfs diff=lfs merge=lfs -text .pt filter=lfs diff=lfs merge=lfs -text .pth filter=lfs diff=lfs merge=lfs -text tfevents filter=lfs diff=lfs merge=lfs -text {"mode":"full","isActive":false}
suhnylla/planes_airlines	suhnylla	2021-07-22T02:21:24Z	69	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: planes_airlines results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.32307693362236023 --- # planes_airlines Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### planes cathay pacific ![planes cathay pacific](images/planes_cathay_pacific.jpg) #### planes delta airlines ![planes delta airlines](images/planes_delta_airlines.jpg) #### planes malaysia airlines ![planes malaysia airlines](images/planes_malaysia_airlines.jpg) #### planes singapore airlines ![planes singapore airlines](images/planes_singapore_airlines.jpg) #### planes virgin airlines ![planes virgin airlines](images/planes_virgin_airlines.jpg)
aristotletan/bart-large-finetuned-xsum	aristotletan	2021-07-22T01:45:40Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:wsj_markets", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - wsj_markets metrics: - rouge model_index: - name: bart-large-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wsj_markets type: wsj_markets args: default metric: name: Rouge1 type: rouge value: 15.3934 --- <!-- 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. --> # bart-large-finetuned-xsum This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the wsj_markets dataset. It achieves the following results on the evaluation set: - Loss: 0.8497 - Rouge1: 15.3934 - Rouge2: 7.0378 - Rougel: 13.9522 - Rougelsum: 14.3541 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.0964 \| 1.0 \| 1735 \| 0.9365 \| 18.703 \| 12.7539 \| 18.1293 \| 18.5397 \| 20.0 \| \| 0.95 \| 2.0 \| 3470 \| 0.8871 \| 19.5223 \| 13.0938 \| 18.9148 \| 18.8363 \| 20.0 \| \| 0.8687 \| 3.0 \| 5205 \| 0.8587 \| 15.0915 \| 7.142 \| 13.6693 \| 14.5975 \| 20.0 \| \| 0.7989 \| 4.0 \| 6940 \| 0.8569 \| 18.243 \| 11.4495 \| 17.4326 \| 17.489 \| 20.0 \| \| 0.7493 \| 5.0 \| 8675 \| 0.8497 \| 15.3934 \| 7.0378 \| 13.9522 \| 14.3541 \| 20.0 \| ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.10.0 - Tokenizers 0.10.3
huggingtweets/devops_guru-neiltyson-nigelthurlow	huggingtweets	2021-07-21T22:55:43Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/devops_guru-neiltyson-nigelthurlow/1626908139492/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1163117736140124160/u23u5DU4_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/748969887146471424/4BmVTQAv_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/74188698/NeilTysonOriginsA-Crop_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nigel Thurlow & Ernest Wright, Ph. D. ABD & Neil deGrasse Tyson</div> <div style="text-align: center; font-size: 14px;">@devops_guru-neiltyson-nigelthurlow</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nigel Thurlow & Ernest Wright, Ph. D. ABD & Neil deGrasse Tyson. \| Data \| Nigel Thurlow \| Ernest Wright, Ph. D. ABD \| Neil deGrasse Tyson \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 1264 \| 1933 \| 3250 \| \| Retweets \| 648 \| 20 \| 10 \| \| Short tweets \| 27 \| 105 \| 79 \| \| Tweets kept \| 589 \| 1808 \| 3161 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jc9vah1k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @devops_guru-neiltyson-nigelthurlow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2myicem9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2myicem9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/devops_guru-neiltyson-nigelthurlow') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
bgfruna/double-bart-ensemble-squad2	bgfruna	2021-07-21T22:47:12Z	0	0	null	[ "pytorch", "question-answering", "en", "dataset:squad_v2", "dataset:squad2", "license:cc-by-4.0", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: en tags: - pytorch - question-answering datasets: - squad_v2 - squad2 license: cc-by-4.0 metrics: - squad_v2 - exact - f1 widget: - text: "By what main attribute are computational problems classified utilizing computational complexity theory?" context: "Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm." --- # Performance This ensemble was evaluated on [SQuAD 2.0](https://huggingface.co/datasets/squad_v2) with the following results: ``` {'HasAns_exact': 52.5472334682861, 'HasAns_f1': 67.94939813758602, 'HasAns_total': 5928, 'NoAns_exact': 91.75777964676199, 'NoAns_f1': 91.75777964676199, 'NoAns_total': 5945, 'best_exact': 72.16373283921503, 'best_exact_thresh': 0.0, 'best_f1': 79.85378860941708, 'best_f1_thresh': 0.0, 'exact': 72.1805777815211, 'f1': 79.87063355172326, 'total': 11873 } ```
huggingtweets/nigelthurlow	huggingtweets	2021-07-21T22:34:57Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/nigelthurlow/1626906893945/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1163117736140124160/u23u5DU4_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nigel Thurlow</div> <div style="text-align: center; font-size: 14px;">@nigelthurlow</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nigel Thurlow. \| Data \| Nigel Thurlow \| \| --- \| --- \| \| Tweets downloaded \| 1264 \| \| Retweets \| 648 \| \| Short tweets \| 27 \| \| Tweets kept \| 589 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/n4jwj2tf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nigelthurlow's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nigelthurlow') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/glownigga	huggingtweets	2021-07-21T22:15:19Z	8	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/glownigga/1626905715267/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1292227674539208704/uNcnG4c3_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">gl0w</div> <div style="text-align: center; font-size: 14px;">@glownigga</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from gl0w. \| Data \| gl0w \| \| --- \| --- \| \| Tweets downloaded \| 3132 \| \| Retweets \| 157 \| \| Short tweets \| 776 \| \| Tweets kept \| 2199 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t0rqzrr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glownigga's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glownigga') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/alicefromqueens	huggingtweets	2021-07-21T21:38:57Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/alicefromqueens/1626903533456/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372804858068230149/aSZcjxvN_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Dread Alice</div> <div style="text-align: center; font-size: 14px;">@alicefromqueens</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Dread Alice. \| Data \| Dread Alice \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 50 \| \| Short tweets \| 511 \| \| Tweets kept \| 2688 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/frqs20kj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alicefromqueens's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c7152gp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c7152gp/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/alicefromqueens') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
b25mayank3/shirt_identifier	b25mayank3	2021-07-21T20:29:09Z	73	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: shirt_identifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.6875 --- # shirt_identifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Big Check shirt ![Big Check shirt](images/Big_Check_shirt.jpg) #### Formal Shirt ![Formal Shirt](images/Formal_Shirt.jpg) #### casual shirt ![casual shirt](images/casual_shirt.jpg) #### denim shirt ![denim shirt](images/denim_shirt.jpg)
flax-community/clip-vit-base-patch32_marian-es	flax-community	2021-07-21T19:30:04Z	1	0	transformers	[ "transformers", "jax", "tensorboard", "clip-vision-marian", "arxiv:2102.08981", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	# CLIP-Vision-Marian Seq2Seq Encoder-Decoder Model Pretrained CLIP-Vision-Marian pre-trained on a subset of Spanish-translated Conceptual-12M image-text pairs using a seq2seq model training objective. 2.5M cleaned English image-text pairs are translated using Spanish Marian Model. We trained CLIP-Vision-Marian model during community week hosted by Huggingface 🤗 using JAX/Flax. ## Model description CLIP-Vision-Marian is a modified transformers model which takes in visual embeddings from CLIP-Vision transformer and feeds into the `encoder_hidden_states` of a Marian decoder. This is done for deep cross-modal interaction via `cross-attention` between the two modes. The decoder then predicts logits for the `input_ids` provided and can be used for generation. ## Intended uses & limitations❗️ You can use the raw model for encoder-decoder network where you want the encoder to encode images and the decoder to decode text. Note that this model is primarily aimed at being fine-tuned on tasks like Spanish image captioning. ### How to use❓ You will need to clone the model from [here](https://github.com/bhavitvyamalik/spanish-image-captioning). An example of usage is shown below: ```python >>> from torchvision.io import read_image >>> import numpy as np >>> import wget >>> import os >>> from transformers import CLIPProcessor, MarianTokenizer >>> from models.flax_clip_vision_marian.modeling_clip_vision_marian import FlaxCLIPVisionMarianMT img = wget.download("https://huggingface.co/streamlitiframe/flax-community/spanish-image-captioning/+/media/55a8898e61131569cc0ed4e72a8b3092969d63c2dff4f47ed9ef0d89.jpeg") >>> img = read_image(img) # reading image >>> clip_processor = CLIPProcessor.from_pretrained('flax-community/clip-vit-base-patch32_marian') >>> clip_outputs = clip_processor(images=img) >>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images. >>> tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-es') >>> model = FlaxCLIPVisionMarianMT.from_pretrained('flax-community/clip-vit-base-patch32_marian-es') >>> output_ids = model.generate(batch["pixel_values"], early_stopping=True, num_beams=4, max_length=64).sequences >>> output_string = tokenizer.batch_decode(output_ids.reshape(-1, 64), skip_special_tokens=True, max_length=64) >>> output_string # Sopa de avena en un tazón blanco con arándanos frescos ``` ## Training data 🏋🏻‍♂️ The Spanish image captioning model was trained on a subset of Conceptual 12M dataset by Google: <br> <br> [Conceptual 12M](https://github.com/google-research-datasets/conceptual-12m), Introduced by Changpinyo et al. in [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981). ### Please update the dataset link here The translated dataset can be downloaded from [conceptual-12m-multilingual-marian-es](https://huggingface.co/datasets/flax-community/conceptual-12m-multilingual-marian-es). We do not provide images as we do not own any of them. One can download images from the `image_url` section of the original Conceptual 12M dataset. ## Data Cleaning 🧹 Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs, out of which we took only 2.5M image, caption pairs. #### Train set: Total data: <br> 2475000 captions <br> 2475000 images <br> #### Validation set Total data: <br> 25000 captions <br> 25000 images <br> ## Training procedure 👨🏻‍💻 ### Training The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) 8 v3 TPU cores for 42K steps with a batch size of 128 and a sequence length of 128. The optimizer used is Adam with a learning rate of 3e-4, β1 = 0.9, β2 = 0.98 and ε = 1e-8, a weight decay of 0.01, learning rate warmup for 1,000 steps and linear decay of the learning rate after. We tracked experiments using Tensorboard which can be found in `Training Metrics` tab. #### Pretraining Results 📊 Our model reached eval loss of ~3.1 around ~20K steps. Here are the BLEU^ scores for different languages: \|Language \|BLEU-1\|BLEU-2\|BLEU-3\|BLEU-4\| \|--------------------------\|------\|------\|------\|------\| \|Spanish \| 0.2015\| 0.1348\| 0.09982\| 0.0748\| ^BLEU scores are out of 1 ## App Demo You can try out our model on 🤗 Huggingface's spaces 🪐 : [Streamlit app of Spanish Image Captioning model on Huggingface Spaces](https://huggingface.co/spaces/flax-community/spanish-image-captioning) ## Team Members - Bhavitvya Malik [@bhavitvyamalik](https://github.com/bhavitvyamalik) - Gunjan Chhablani [@gchhablani](https://github.com/gchhablani) ## Credits Thanks to Huggingface 🤗 & Google JAX/Flax team for such a wonderful community week. Big thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@patil-suraj](https://github.com/patil-suraj) for helping us with our solution during the community week. <img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
AIDA-UPM/MSTSb_paraphrase-multilingual-MiniLM-L12-v2	AIDA-UPM	2021-07-21T18:02:53Z	22	0	sentence-transformers	[ "sentence-transformers", "pytorch", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 1438 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 2, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 288, "weight_decay": 0.05 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
flax-community/gpt2-Cosmos	flax-community	2021-07-21T16:58:00Z	1	0	transformers	[ "transformers", "jax", "tensorboard", "gpt2", "arxiv:1909.00277", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	# Cosmos QA (gpt2) > This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/train-a-gpt2-model-for-contextual-common-sense-reasoning-using-the-cosmos-qa-dataset/7463), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team Members -Rohan V Kashyap ([Rohan](https://huggingface.co/Rohan)) -Vivek V Kashyap ([Vivek](https://huggingface.co/Vivek)) ## Dataset [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://huggingface.co/datasets/cosmos_qa).This dataset contains a set of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly.The questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives. ### Example ```json {"Context":["It's a very humbling experience when you need someone to dress you every morning, tie your shoes, and put your hair up. Every menial task takes an unprecedented amount of effort. It made me appreciate Dan even more. But anyway I shan't dwell on this (I'm not dying after all) and not let it detract from my lovely 5 days with my friends visiting from Jersey."], "Question":["What's a possible reason the writer needed someone to dress him every morning?"], "Multiple Choice":["A: The writer doesn't like putting effort into these tasks.", "B: The writer has a physical disability.", "C: The writer is bad at doing his own hair.", "D: None of the above choices."] "link":"https://arxiv.org/pdf/1909.00277.pdf" } ``` ## How to use ```bash # Installing requirements pip install transformers pip install datasets ``` ```python from model_file import FlaxGPT2ForMultipleChoice from datasets import Dataset model_path="flax-community/gpt2-Cosmos" model = FlaxGPT2ForMultipleChoice.from_pretrained(model_path,input_shape=(1,4,1)) dataset=Dataset.from_csv('./') def preprocess(example): example['context&question']=example['context']+example['question'] example['first_sentence']=[example['context&question'],example['context&question'],example['context&question'],example['context&question']] example['second_sentence']=example['answer0'],example['answer1'],example['answer2'],example['answer3'] return example dataset=dataset.map(preprocess) def tokenize(examples): a=tokenizer(examples['first_sentence'],examples['second_sentence'],padding='max_length',truncation=True,max_length=256,return_tensors='jax') a['labels']=examples['label'] return a dataset=dataset.map(tokenize) input_id=jnp.array(dataset['input_ids']) att_mask=jnp.array(dataset['attention_mask']) outputs=model(input_id,att_mask) final_output=jnp.argmax(outputs,axis=-1) print(f"the predction of the dataset : {final_output}") ``` ``` The Correct answer:-Option 1 ``` ## Preprocessing The texts are tokenized using the GPT2 tokenizer.To feed the inputs of multiple choice we concatenated context and question as first input and all the 4 possible choices as the second input to our tokenizer. ## Evaluation The following tables summarize the scores obtained by the GPT2-CosmosQA.The ones marked as (^) are the baseline models. \| Model \| Dev Acc \| Test Acc \| \|:---------------:\|:-----:\|:-----:\| \| BERT-FT Multiway^\| 68.3.\| 68.4 \| \| GPT-FT ^ \| 54.0 \| 54.4. \| \| GPT2-CosmosQA \| 60.3 \| 59.7 \| ## Inference This project was mainly to test the common sense understanding of the GPT2-model.We finetuned on a Dataset known as CosmosQ requires reasoning beyond the exact text spans in the context.The above results shows that GPT2 model is doing better than most of the base line models given that it only used to predict the next word in the pre-training objective. ## Credits Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week. Especially for providing such massive computing resource. Big thanks to [@patil-suraj](https://github.com/patil-suraj) & [@patrickvonplaten](https://github.com/patrickvonplaten) for mentoring during whole week.
ktangri/gpt-neo-demo	ktangri	2021-07-21T15:20:09Z	10	1	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "text generation", "the Pile", "causal-lm", "en", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - en tags: - text generation - pytorch - the Pile - causal-lm license: apache-2.0 datasets: - the Pile --- # GPT-Neo 2.7B (By EleutherAI) ## Model Description GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. ## Training data GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. ## Training procedure This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. ## Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B') >>> generator("EleutherAI has", do_sample=True, min_length=50) [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Eval results All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM). ### Linguistic Reasoning \| Model and Size \| Pile BPB \| Pile PPL \| Wikitext PPL \| Lambada PPL \| Lambada Acc \| Winogrande \| Hellaswag \| \| ---------------- \| ---------- \| ---------- \| ------------- \| ----------- \| ----------- \| ---------- \| ----------- \| \| GPT-Neo 1.3B \| 0.7527 \| 6.159 \| 13.10 \| 7.498 \| 57.23% \| 55.01% \| 38.66% \| \| GPT-2 1.5B \| 1.0468 \| ----- \| 17.48 \| 10.634 \| 51.21% \| 59.40% \| 40.03% \| \| GPT-Neo 2.7B \| 0.7165 \| 5.646 \| 11.39 \| 5.626 \| 62.22% \| 56.50% \| 42.73% \| \| GPT-3 Ada \| 0.9631 \| ----- \| ----- \| 9.954 \| 51.60% \| 52.90% \| 35.93% \| ### Physical and Scientific Reasoning \| Model and Size \| MathQA \| PubMedQA \| Piqa \| \| ---------------- \| ---------- \| ---------- \| ----------- \| \| GPT-Neo 1.3B \| 24.05% \| 54.40% \| 71.11% \| \| GPT-2 1.5B \| 23.64% \| 58.33% \| 70.78% \| \| GPT-Neo 2.7B \| 24.72% \| 57.54% \| 72.14% \| \| GPT-3 Ada \| 24.29% \| 52.80% \| 68.88% \| ### Down-Stream Applications TBD ### BibTeX entry and citation info To cite this model, use ```bibtex @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` To cite the codebase that this model was trained with, use ```bibtex @software{gpt-neo, author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}, url = {http://github.com/eleutherai/gpt-neo}, version = {1.0}, year = {2021}, } ```
ifis-zork/ZORK_AI_SCI_FI	ifis-zork	2021-07-21T14:18:15Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model_index: - name: ZORK_AI_SCI_FI results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_SCI_FI This model is a fine-tuned version of [ifis-zork/ZORK_AI_SCI_FI_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_SCI_FI_TEMP) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
bipin/malayalam-news-classifier	bipin	2021-07-21T13:40:25Z	9	3	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "malayalam", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - text-classification - roberta - malayalam - pytorch widget: - text: "2032 ഒളിമ്പിക്‌സിന് ബ്രിസ്‌ബെയ്ന്‍ വേദിയാകും; ഗെയിംസിന് വേദിയാകുന്ന മൂന്നാമത്തെ ഓസ്‌ട്രേലിയന്‍ നഗരം" --- ## Malayalam news classifier ### Overview This model is trained on top of [MalayalamBert](https://huggingface.co/eliasedwin7/MalayalamBERT) for the task of classifying malayalam news headlines. Presently, the following news categories are supported: * Business * Sports * Entertainment ### Dataset The dataset used for training this model can be found [here](https://www.kaggle.com/disisbig/malyalam-news-dataset). ### Using the model with HF pipeline ```python from transformers import pipeline news_headline = "ക്രിപ്‌റ്റോ ഇടപാടുകളുടെ വിവരങ്ങൾ ആവശ്യപ്പെട്ട് ആദായനികുതി വകുപ്പ് നോട്ടീസയച്ചു" model = pipeline(task="text-classification", model="bipin/malayalam-news-classifier") model(news_headline) # Output # [{'label': 'business', 'score': 0.9979357123374939}] ``` ### Contact For feedback and questions, feel free to contact via twitter [@bkrish_](https://twitter.com/bkrish_)
defex/distilgpt2-finetuned-amazon-reviews	defex	2021-07-21T10:36:15Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - null model_index: - name: distilgpt2-finetuned-amazon-reviews results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # distilgpt2-finetuned-amazon-reviews This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Datasets 1.9.0 - Tokenizers 0.10.3
ifis-zork/ZORK_AI_FANTASY	ifis-zork	2021-07-21T09:50:17Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model_index: - name: ZORK_AI_FANTASY results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_FANTASY This model is a fine-tuned version of [ifis-zork/ZORK_AI_FAN_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_FAN_TEMP) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
flax-community/clip-vision-bert-vqa-ft-6k	flax-community	2021-07-21T09:21:58Z	4	4	transformers	[ "transformers", "jax", "clip-vision-bert", "text-classification", "arxiv:1908.03557", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# CLIP-Vision-BERT Multilingual VQA Model Fine-tuned CLIP-Vision-BERT on translated [VQAv2](https://visualqa.org/challenge.html) image-text pairs using sequence classification objective. We translate the dataset to three other languages other than English: French, German, and Spanish using the [MarianMT Models](https://huggingface.co/transformers/model_doc/marian.html). This model is based on the VisualBERT which was introduced in [this paper](https://arxiv.org/abs/1908.03557) and first released in [this repository](https://github.com/uclanlp/visualbert). The output is 3129 class logits, the same classes as used by VisualBERT authors. The initial weights are loaded from the Conceptual-12M 60k [checkpoints](https://huggingface.co/flax-community/clip-vision-bert-cc12m-60k). We trained the CLIP-Vision-BERT VQA model during community week hosted by Huggingface 🤗 using JAX/Flax. ## Model description CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from the CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes. ## Intended uses & limitations❗️ This model is fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since VQAv2 is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model. ### How to use❓ You can use this model directly on visual question answering. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below: ```python >>> from torchvision.io import read_image >>> import numpy as np >>> import os >>> from transformers import CLIPProcessor, BertTokenizerFast >>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForSequenceClassification >>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0]) >>> img = read_image(image_path) >>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32') ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy. >>> clip_outputs = clip_processor(images=img) >>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images. >>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased') >>> model = FlaxCLIPVisionBertForSequenceClassification.from_pretrained('flax-community/clip-vision-bert-vqa-ft-6k') >>> text = "Are there teddy bears in the image?" >>> tokens = tokenizer([text], return_tensors="np") >>> pixel_values = np.concatenate([clip_outputs['pixel_values']]) >>> outputs = model(pixel_values=pixel_values, tokens) >>> preds = outputs.logits[0] >>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores >>> top_5_indices = sorted_indices[:5] >>> top_5_tokens = list(map(model.config.id2label.get,top_5_indices)) >>> top_5_scores = preds[top_5_indices] >>> print(dict(zip(top_5_tokens, top_5_scores))) {'yes': 15.809224, 'no': 7.8785815, '<unk>': 4.622649, 'very': 4.511462, 'neither': 3.600822} ``` ## Training data 🏋🏻‍♂️ The CLIP-Vision-BERT model was fine-tuned on the translated version of the VQAv2 dataset in four languages using Marian: English, French, German and Spanish. Hence, the dataset is four times the original English questions. The dataset questions and image URLs/paths can be downloaded from [flax-community/multilingual-vqa](https://huggingface.co/datasets/flax-community/multilingual-vqa). ## Data Cleaning 🧹 Though the original dataset contains 443,757 train and 214,354 validation image-question pairs. We only use the `multiple_choice_answer`. The answers which are not present in the 3129 classes are mapped to the `<unk>` label. Splits We use the original train-val splits from the VQAv2 dataset. After translation, we get 1,775,028 train image-text pairs, and 857,416 validation image-text pairs. ## Training procedure 👨🏻‍💻 ### Preprocessing The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[SEP]`. ### Fine-tuning The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) 8 v3 TPU cores for 6k steps with a per device batch size of 128 and a max sequence length of 128. The optimizer used is AdamW with a learning rate of 5e-5, learning rate warmup for 1600 steps, and linear decay of the learning rate after. We tracked experiments using TensorBoard. Here is link to main dashboard: [CLIP Vision BERT VQAv2 Fine-tuning Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard) #### Fine-tuning Results 📊 The model at this checkpoint reached eval accuracy of 0.49** on our multilingual VQAv2 dataset. ## Team Members - Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani) - Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik) ## Acknowledgements We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him. This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project. Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs. <img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
huggingtweets/plesmasquerade	huggingtweets	2021-07-21T02:40:45Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/plesmasquerade/1626834982015/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415803411002314752/X0K3MR1R_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪</div> <div style="text-align: center; font-size: 14px;">@plesmasquerade</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪. \| Data \| lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪 \| \| --- \| --- \| \| Tweets downloaded \| 3235 \| \| Retweets \| 1376 \| \| Short tweets \| 330 \| \| Tweets kept \| 1529 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39gtjjjo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @plesmasquerade's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6jt0gb2r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6jt0gb2r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/plesmasquerade') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lg/ghpy_20k	lg	2021-07-20T23:55:56Z	10	2	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	This model is provided with no guarantees whatsoever; use at your own risk. This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure code.
espnet/kan-bayashi_csmsc_conformer_fastspeech2	espnet	2021-07-20T21:31:29Z	8	1	espnet	[ "espnet", "audio", "text-to-speech", "zh", "dataset:csmsc", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2022-03-02T23:29:05Z	--- tags: - espnet - audio - text-to-speech language: zh datasets: - csmsc license: cc-by-4.0 --- ## ESPnet2 TTS pretrained model ### `kan-bayashi/csmsc_conformer_fastspeech2` ♻️ Imported from https://zenodo.org/record/4031955/ This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ifis-zork/ZORK_AI_MODERN	ifis-zork	2021-07-20T20:47:22Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model_index: - name: ZORK_AI_MODERN results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_MODERN This model is a fine-tuned version of [ifis-zork/ZORK_AI_MODERN_A](https://huggingface.co/ifis-zork/ZORK_AI_MODERN_A) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
ifis-zork/ZORK_AI_FAN_TEMP	ifis-zork	2021-07-20T19:58:38Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model_index: - name: ZORK_AI_FAN_TEMP results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_FAN_TEMP This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
ifis-zork/ZORK_AI_MODERN_A	ifis-zork	2021-07-20T19:37:56Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer model_index: - name: ZORK_AI_MODERN_A results: - task: name: Causal Language Modeling type: text-generation --- <!-- 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. --> # ZORK_AI_MODERN_A This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.8.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
Amrrs/south-indian-foods	Amrrs	2021-07-20T18:22:24Z	76	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:04Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: south-indian-foods results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.875 --- # south-indian-foods Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### dosai ![dosai](images/dosai.jpg) #### idiyappam ![idiyappam](images/idiyappam.jpg) #### idli ![idli](images/idli.jpg) #### puttu ![puttu](images/puttu.jpg) #### vadai ![vadai](images/vadai.jpg)
huggingtweets/sharsenko	huggingtweets	2021-07-20T16:08:39Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/sharsenko/1626797315466/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1411529618180431873/Eyc2bjZV_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Willo</div> <div style="text-align: center; font-size: 14px;">@sharsenko</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Willo. \| Data \| Willo \| \| --- \| --- \| \| Tweets downloaded \| 1279 \| \| Retweets \| 304 \| \| Short tweets \| 219 \| \| Tweets kept \| 756 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r0bziin/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sharsenko's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37iziw4p) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37iziw4p/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sharsenko') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ritog/robi-kobi	ritog	2021-07-20T15:25:11Z	4	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "bn", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: bn tags: - text-generation widget: - text: তোমাকে দেখেছি আমার হৃদয় মাঝে --- # Robi Kobi ### Created by [Ritobrata Ghosh](https://ghosh-r.github.io) A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore. This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the [Bangla GPT-2](https://huggingface.co/ghosh-r/bangla-gpt2) pretrained model, trained on mc4-Bengali dataset.
ritog/bangla-gpt2	ritog	2021-07-20T15:22:47Z	12	2	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "bn", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: bn tags: - text-generation widget: - text: আজ একটি সুন্দর দিন এবং আমি --- # Bangla-GPT2 ### A GPT-2 Model for the Bengali Language * Dataset- mc4 Bengali * Training time- ~40 hours * Written in- JAX If you use this model, please cite: ``` @misc{bangla-gpt2, author = {Ritobrata Ghosh}, year = {2016}, title = {Bangla GPT-2}, publisher = {Hugging Face} } ```
huggingtweets/_nalian-simondiamondxx	huggingtweets	2021-07-20T14:18:20Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://www.huggingtweets.com/_nalian-simondiamondxx/1626790677014/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375921626261434374/S5xS0GV6_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1385553334468218882/kTwhzZRu_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">SimonDiamond ♡VTUBER♡ & Nalian</div> <div style="text-align: center; font-size: 14px;">@_nalian-simondiamondxx</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from SimonDiamond ♡VTUBER♡ & Nalian. \| Data \| SimonDiamond ♡VTUBER♡ \| Nalian \| \| --- \| --- \| --- \| \| Tweets downloaded \| 1867 \| 3238 \| \| Retweets \| 258 \| 137 \| \| Short tweets \| 456 \| 541 \| \| Tweets kept \| 1153 \| 2560 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hbmyc94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_nalian-simondiamondxx's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lmxvuzx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lmxvuzx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/_nalian-simondiamondxx') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

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