librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 06:27:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 06:27:26	card stringlengths 11 1.01M
YassineB/test_Resnet	YassineB	2022-10-04T14:34:56Z	41	0	transformers	[ "transformers", "resnet", "image-classification", "vision", "dataset:imagenet-1k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-04T14:10:44Z	--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k ---
esc-benchmark/conformer-rnnt-common_voice	esc-benchmark	2022-10-04T14:31:55Z	1	0	nemo	[ "nemo", "esc", "en", "dataset:common_voice", "region:us" ]	null	2022-10-04T14:31:39Z	--- language: - en tags: - esc datasets: - common_voice --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \ --config_path="conf/conformer_transducer_bpe_xlarge.yaml" \ --model_name_or_path="stt_en_conformer_transducer_xlarge" \ --dataset_name="esc-benchmark/esc-datasets" \ --tokenizer_path="tokenizer" \ --vocab_size="1024" \ --max_steps="100000" \ --dataset_config_name="common_voice" \ --output_dir="./" \ --run_name="conformer-rnnt-common-voice" \ --wandb_project="rnnt" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="4" \ --logging_steps="50" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --save_strategy="steps" \ --save_steps="20000" \ --evaluation_strategy="steps" \ --eval_steps="20000" \ --report_to="wandb" \ --preprocessing_num_workers="4" \ --fused_batch_size="4" \ --length_column_name="input_lengths" \ --max_eval_duration_in_seconds="20" \ --fuse_loss_wer \ --group_by_length \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --use_auth_token ```
esc-benchmark/whisper-aed-switchboard	esc-benchmark	2022-10-04T14:24:16Z	0	0	null	[ "esc", "en", "dataset:switchboard", "region:us" ]	null	2022-10-04T14:23:58Z	--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="switchboard" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-switchboard" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-switchboard" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```
esc-benchmark/whisper-aed-ami	esc-benchmark	2022-10-04T14:17:38Z	0	0	null	[ "esc", "en", "dataset:ami", "region:us" ]	null	2022-10-04T14:17:20Z	--- language: - en tags: - esc datasets: - ami --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="ami" \ --max_steps="2500" \ --output_dir="./" \ --run_name="whisper-ami" \ --dropout_rate="0.1" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="500" \ --save_strategy="steps" \ --save_steps="500" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```
esc-benchmark/whisper-aed-earnings22	esc-benchmark	2022-10-04T14:14:02Z	0	0	null	[ "esc", "en", "dataset:earnings22", "region:us" ]	null	2022-10-04T14:13:44Z	--- language: - en tags: - esc datasets: - earnings22 --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="earnings22" \ --max_steps="2500" \ --output_dir="./" \ --run_name="whisper-earnings22" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="500" \ --save_strategy="steps" \ --save_steps="500" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```
esc-benchmark/whisper-aed-gigaspeech	esc-benchmark	2022-10-04T14:06:02Z	0	0	null	[ "esc", "en", "dataset:gigaspeech", "region:us" ]	null	2022-10-04T14:05:45Z	--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="gigaspeech" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-gigaspeech" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```
melll-uff/bertweetbr	melll-uff	2022-10-04T14:00:33Z	379	10	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-29T21:10:30Z	--- language: pt license: apache-2.0 --- # <a name="introduction"></a> BERTweet.BR: A Pre-Trained Language Model for Tweets in Portuguese Having the same architecture of [BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet) we trained our model from scratch following [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) pre-training procedure on a corpus of approximately 9GB containing 100M Portuguese Tweets. ## Usage ### Normalized Inputs ```python import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) # INPUT TWEETS ALREADY NORMALIZED! inputs = [ "Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:", "Que jogo ontem @USER :mãos_juntas:", "Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL"] encoded_inputs = tokenizer(inputs, return_tensors="pt", padding=True) with torch.no_grad(): last_hidden_states = model(encoded_inputs) # CLS Token of last hidden states. Shape: (number of input sentences, hidden sizeof the model) last_hidden_states[0][:,0,:] tensor([[-0.1430, -0.1325, 0.1595, ..., -0.0802, -0.0153, -0.1358], [-0.0108, 0.1415, 0.0695, ..., 0.1420, 0.1153, -0.0176], [-0.1854, 0.1866, 0.3163, ..., -0.2117, 0.2123, -0.1907]]) ``` ### Normalize raw input Tweets ```python from emoji import demojize import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=True) inputs = [ "Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim 🎵", "Que jogo ontem @cristiano 🙏", "Demojizer para Python é 👍 e está disponível em https://pypi.org/project/emoji/"] tokenizer.demojizer = lambda x: demojize(x, language='pt') [tokenizer.normalizeTweet(s) for s in inputs] # Tokenizer first normalizes tweet sentences ['Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:', 'Que jogo ontem @USER :mãos_juntas:', 'Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL'] encoded_inputs = tokenizer(inputs, return_tensors="pt", padding=True) with torch.no_grad(): last_hidden_states = model(encoded_inputs) # CLS Token of last hidden states. Shape: (number of input sentences, hidden sizeof the model) last_hidden_states[0][:,0,:] tensor([[-0.1430, -0.1325, 0.1595, ..., -0.0802, -0.0153, -0.1358], [-0.0108, 0.1415, 0.0695, ..., 0.1420, 0.1153, -0.0176], [-0.1854, 0.1866, 0.3163, ..., -0.2117, 0.2123, -0.1907]]) ``` ### Mask Filling with Pipeline ```python from transformers import pipeline model_name = 'melll-uff/bertweetbr' tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) filler_mask = pipeline("fill-mask", model=model_name, tokenizer=tokenizer) filler_mask("Rio é a <mask> cidade do Brasil.", top_k=5) # Output [{'sequence': 'Rio é a melhor cidade do Brasil.', 'score': 0.9871652126312256, 'token': 120, 'token_str': 'm e l h o r'}, {'sequence': 'Rio é a pior cidade do Brasil.', 'score': 0.005050931591540575, 'token': 316, 'token_str': 'p i o r'}, {'sequence': 'Rio é a maior cidade do Brasil.', 'score': 0.004420778248459101, 'token': 389, 'token_str': 'm a i o r'}, {'sequence': 'Rio é a minha cidade do Brasil.', 'score': 0.0021856199018657207, 'token': 38, 'token_str': 'm i n h a'}, {'sequence': 'Rio é a segunda cidade do Brasil.', 'score': 0.0002110043278662488, 'token': 667, 'token_str': 's e g u n d a'}] ```
esc-benchmark/whisper-aed-librispeech	esc-benchmark	2022-10-04T13:49:46Z	0	0	null	[ "esc", "en", "dataset:librispeech", "region:us" ]	null	2022-10-04T13:49:29Z	--- language: - en tags: - esc datasets: - librispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="librispeech" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-librispeech" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```
esc-benchmark/wav2vec2-aed-chime4	esc-benchmark	2022-10-04T13:46:34Z	4	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:chime4", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:46:20Z	--- language: - en tags: - esc datasets: - chime4 --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="chime4" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-chime4" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="4" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="250" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.6" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
nayan06/buy-others1	nayan06	2022-10-04T13:46:25Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-04T13:46:14Z	--- 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 768 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, mean 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 100 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 100, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, '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 -->
esc-benchmark/wav2vec2-aed-switchboard	esc-benchmark	2022-10-04T13:44:32Z	4	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:switchboard", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:44:18Z	--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="switchboard" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-switchboard" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="260" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.8" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-aed-spgispeech	esc-benchmark	2022-10-04T13:38:15Z	5	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:spgispeech", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:38:01Z	--- language: - en tags: - esc datasets: - spgispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="spgispeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-spgispeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="225" \ --final_generation_num_beams="14" \ --generation_length_penalty="1.6" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-aed-gigaspeech	esc-benchmark	2022-10-04T13:35:54Z	7	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:gigaspeech", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:35:41Z	--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="gigaspeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-gigaspeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="200" \ --final_generation_num_beams="14" \ --generation_length_penalty="1.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-aed-voxpopuli	esc-benchmark	2022-10-04T13:33:40Z	5	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:voxpopuli", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:33:27Z	--- language: - en tags: - esc datasets: - voxpopuli --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="voxpopuli" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-voxpopuli" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="1" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="10001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="225" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.8" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-aed-librispeech	esc-benchmark	2022-10-04T13:27:46Z	5	0	transformers	[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:librispeech", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:27:32Z	--- language: - en tags: - esc datasets: - librispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="librispeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-librispeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="300" \ --final_generation_num_beams="12" \ --generation_length_penalty="1.6" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-ctc-chime4	esc-benchmark	2022-10-04T13:23:26Z	4	0	transformers	[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:chime4", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:23:19Z	--- language: - en tags: - esc datasets: - chime4 --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-chime4-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="chime4" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-chime4" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-ctc-switchboard	esc-benchmark	2022-10-04T13:22:21Z	5	0	transformers	[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:switchboard", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:22:13Z	--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-switchboard-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="switchboard" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-switchboard" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```
esc-benchmark/wav2vec2-ctc-ami	esc-benchmark	2022-10-04T13:21:19Z	4	0	transformers	[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:ami", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:21:11Z	--- language: - en tags: - esc datasets: - ami --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-ami-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="ami" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-ami" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```
Akshata/autotrain-person-name-validity1-1655358687	Akshata	2022-10-04T13:17:17Z	99	0	transformers	[ "transformers", "pytorch", "autotrain", "token-classification", "en", "dataset:Akshata/autotrain-data-person-name-validity1", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	token-classification	2022-10-04T13:15:05Z	--- tags: - autotrain - token-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - Akshata/autotrain-data-person-name-validity1 co2_eq_emissions: emissions: 0.015012024821802214 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1655358687 - CO2 Emissions (in grams): 0.0150 ## Validation Metrics - Loss: 0.038 - Accuracy: 0.991 - Precision: 0.000 - Recall: 0.000 - F1: 0.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Akshata/autotrain-person-name-validity1-1655358687 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
esc-benchmark/wav2vec2-ctc-gigaspeech	esc-benchmark	2022-10-04T13:15:20Z	4	0	transformers	[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:gigaspeech", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T13:15:12Z	--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-gigaspeech-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="gigaspeech" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-gigaspeech" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```
bharadwajkg/finetuning-cardiffnlp-twitter-roberta-base-sentiment	bharadwajkg	2022-10-04T12:28:23Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-04T10:47:00Z	--- tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: finetuning-cardiffnlp-twitter-roberta-base-sentiment results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.7433333333333333 - name: F1 type: f1 value: 0.7418048347838402 --- <!-- 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. --> # finetuning-cardiffnlp-twitter-roberta-base-sentiment This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 2.0244 - Accuracy: 0.7433 - F1: 0.7418 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
alessandroen/asr_test	alessandroen	2022-10-04T11:10:56Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-23T10:46:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: asr_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # asr_test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
bharadwajkg/finetuning-sentiment-model-3000-samples-imdb	bharadwajkg	2022-10-04T10:26:13Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-04T10:12:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8733333333333333 - name: F1 type: f1 value: 0.8741721854304636 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3054 - Accuracy: 0.8733 - F1: 0.8742 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Nithiwat/mdeberta-v3-base_claim-detection	Nithiwat	2022-10-04T10:24:41Z	100	1	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "claim-detection", "en", "dataset:Nithiwat/claim-detection", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-04T07:42:30Z	--- language: - en tags: - text-classification - claim-detection license: "mit" datasets: - Nithiwat/claim-detection widget: - text: "This is the best cast iron skillet you will ever buy." - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday." - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book" ---
openai/clip-vit-large-patch14-336	openai	2022-10-04T09:41:39Z	4,489,460	223	transformers	[ "transformers", "pytorch", "tf", "clip", "zero-shot-image-classification", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2022-04-22T14:57:43Z	--- tags: - generated_from_keras_callback widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog model-index: - name: clip-vit-large-patch14-336 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # clip-vit-large-patch14-336 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.21.3 - TensorFlow 2.8.2 - Tokenizers 0.12.1
mouss/autotrain-damages-1652858619	mouss	2022-10-04T09:41:15Z	191	0	transformers	[ "transformers", "pytorch", "autotrain", "vision", "image-classification", "dataset:mouss/autotrain-data-damages", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	image-classification	2022-10-04T09:39:50Z	--- tags: - autotrain - vision - image-classification datasets: - mouss/autotrain-data-damages widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.007316433431312107 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1652858619 - CO2 Emissions (in grams): 0.0073 ## Validation Metrics - Loss: 0.082 - Accuracy: 0.989 - Precision: 1.000 - Recall: 0.978 - AUC: 0.995 - F1: 0.989
GItaf/bert-base-uncased-bert-base-uncased-mc-weight1-epoch15	GItaf	2022-10-04T09:32:23Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-10-02T15:01:50Z	--- tags: - generated_from_trainer model-index: - name: bert-base-uncased-bert-base-uncased-mc-weight1-epoch15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-bert-base-uncased-mc-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.8027 - Cls loss: 3.4449 - Lm loss: 4.3556 - Cls Accuracy: 0.5706 - Cls F1: 0.5697 - Cls Precision: 0.5753 - Cls Recall: 0.5706 - Perplexity: 77.91 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 6.9526 \| 1.0 \| 3470 \| 6.3154 \| 1.7748 \| 4.5399 \| 0.4991 \| 0.4577 \| 0.4421 \| 0.4991 \| 93.68 \| \| 6.0876 \| 2.0 \| 6940 \| 6.1427 \| 1.6773 \| 4.4643 \| 0.5545 \| 0.5342 \| 0.5717 \| 0.5545 \| 86.86 \| \| 5.7231 \| 3.0 \| 10410 \| 6.0206 \| 1.5955 \| 4.4240 \| 0.5902 \| 0.5759 \| 0.6020 \| 0.5902 \| 83.43 \| \| 5.3877 \| 4.0 \| 13880 \| 5.9857 \| 1.5772 \| 4.4073 \| 0.6092 \| 0.6031 \| 0.6052 \| 0.6092 \| 82.05 \| \| 5.1092 \| 5.0 \| 17350 \| 6.3742 \| 1.9981 \| 4.3748 \| 0.5942 \| 0.5901 \| 0.5964 \| 0.5942 \| 79.42 \| \| 4.8504 \| 6.0 \| 20820 \| 6.4511 \| 2.0776 \| 4.3737 \| 0.5890 \| 0.5875 \| 0.6041 \| 0.5890 \| 79.34 \| \| 4.6369 \| 7.0 \| 24290 \| 6.9857 \| 2.6268 \| 4.3571 \| 0.5827 \| 0.5796 \| 0.5979 \| 0.5827 \| 78.03 \| \| 4.4667 \| 8.0 \| 27760 \| 6.9550 \| 2.6075 \| 4.3458 \| 0.5833 \| 0.5831 \| 0.5904 \| 0.5833 \| 77.16 \| \| 4.3127 \| 9.0 \| 31230 \| 7.2041 \| 2.8518 \| 4.3504 \| 0.5902 \| 0.5856 \| 0.5935 \| 0.5902 \| 77.51 \| \| 4.1777 \| 10.0 \| 34700 \| 7.4233 \| 3.0746 \| 4.3467 \| 0.5793 \| 0.5770 \| 0.5829 \| 0.5793 \| 77.22 \| \| 4.0871 \| 11.0 \| 38170 \| 7.4997 \| 3.1488 \| 4.3489 \| 0.5746 \| 0.5749 \| 0.5853 \| 0.5746 \| 77.39 \| \| 3.9991 \| 12.0 \| 41640 \| 7.6636 \| 3.3113 \| 4.3502 \| 0.5602 \| 0.5605 \| 0.5676 \| 0.5602 \| 77.49 \| \| 3.9461 \| 13.0 \| 45110 \| 7.6065 \| 3.2514 \| 4.3530 \| 0.5695 \| 0.5690 \| 0.5738 \| 0.5695 \| 77.71 \| \| 3.9013 \| 14.0 \| 48580 \| 7.7562 \| 3.4017 \| 4.3523 \| 0.5787 \| 0.5785 \| 0.5823 \| 0.5787 \| 77.65 \| \| 3.8731 \| 15.0 \| 52050 \| 7.8027 \| 3.4449 \| 4.3556 \| 0.5706 \| 0.5697 \| 0.5753 \| 0.5706 \| 77.91 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
flair/ner-multi-fast	flair	2022-10-04T09:19:01Z	198	6	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "de", "nl", "es", "dataset:conll2003", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - flair - token-classification - sequence-tagger-model language: - en - de - nl - es datasets: - conll2003 widget: - text: "George Washington ging nach Washington" --- ## 4-Language NER in Flair (English, German, Dutch and Spanish) This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French. F1-Score: 91,51 (CoNLL-03 English), 85,72 (CoNLL-03 German revised), 86,22 (CoNLL-03 Dutch), 85,78 (CoNLL-03 Spanish) Predicts 4 tags: \| tag \| meaning \| \|---------------------------------\|-----------\| \| PER \| person name \| \| LOC \| location name \| \| ORG \| organization name \| \| MISC \| other name \| Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-multi-fast") # make example sentence in any of the four languages sentence = Sentence("George Washington ging nach Washington") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [1,2]: "George Washington" [− Labels: PER (0.9977)] Span [5]: "Washington" [− Labels: LOC (0.9895)] ``` So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington ging nach Washington". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the multi-language corpus corpus: Corpus = MultiCorpus([ CONLL_03(), # English corpus CONLL_03_GERMAN(), # German corpus CONLL_03_DUTCH(), # Dutch corpus CONLL_03_SPANISH(), # Spanish corpus ]) # 2. what tag do we want to predict? tag_type = 'ner' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # GloVe embeddings WordEmbeddings('glove'), # FastText embeddings WordEmbeddings('de'), # contextual string embeddings, forward FlairEmbeddings('multi-forward-fast'), # contextual string embeddings, backward FlairEmbeddings('multi-backward-fast'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-multi-fast', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following papers when using this model. ``` @misc{akbik2019multilingual, title={Multilingual sequence labeling with one model}, author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland} booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop}, year = {2019} } ``` ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ```
sd-concepts-library/crb-surrealz	sd-concepts-library	2022-10-04T08:59:07Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-10-04T08:59:02Z	--- license: mit --- ### crb-surrealz on Stable Diffusion This is the `<crbsurreal>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<crbsurreal> 0](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/2.jpeg) ![<crbsurreal> 1](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/4.jpeg) ![<crbsurreal> 2](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/11.jpeg) ![<crbsurreal> 3](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/1.jpeg) ![<crbsurreal> 4](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/9.jpeg) ![<crbsurreal> 5](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/8.jpeg) ![<crbsurreal> 6](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/3.jpeg) ![<crbsurreal> 7](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/0.jpeg) ![<crbsurreal> 8](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/7.jpeg) ![<crbsurreal> 9](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/5.jpeg) ![<crbsurreal> 10](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/10.jpeg) ![<crbsurreal> 11](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/6.jpeg)
eunyounglee/wav2vec_korean	eunyounglee	2022-10-04T05:53:52Z	16	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-04T03:25:17Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec_korean results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_korean This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.13.0
farzeen/ppo-LunarLander-v2	farzeen	2022-10-04T05:23:46Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-04T05:23:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 249.94 +/- 23.25 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
anas-awadalla/gpt2-medium-span-head-finetuned-squad	anas-awadalla	2022-10-04T05:08:07Z	475	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2022-09-26T20:15:16Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-medium-span-head-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-medium-span-head-finetuned-squad This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the squad 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: 3e-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: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
Hasanmurad/banglabert-bert-finetuned-ner	Hasanmurad	2022-10-04T05:07:40Z	89	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-04T04:43:17Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: banglabert-bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # banglabert-bert-finetuned-ner This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9526 - Precision: 0.0143 - Recall: 0.0769 - F1: 0.0241 - Accuracy: 0.0143 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 1 \| 2.0085 \| 0.0143 \| 0.0769 \| 0.0241 \| 0.0143 \| \| No log \| 2.0 \| 2 \| 1.9711 \| 0.0143 \| 0.0769 \| 0.0241 \| 0.0143 \| \| No log \| 3.0 \| 3 \| 1.9526 \| 0.0143 \| 0.0769 \| 0.0241 \| 0.0143 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
KES/ENG-TEC	KES	2022-10-04T05:04:39Z	113	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "Trinidadian Creole", "Caribbean dialect", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-02T14:47:01Z	--- tags: - text2text-generation - Trinidadian Creole - Caribbean dialect license: apache-2.0 --- # Standard English to Trinidad English Creole Translator This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of English to Trinidad English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/). ___ # Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC") model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC") text = "Where are you going now?" inputs = tokenizer("eng:"+text, truncation=True, return_tensors='pt') output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True) translation=tokenizer.batch_decode(output, skip_special_tokens=True) print("".join(translation)) #translation: Weh yuh going now. ``` ___
jon-fernandes/vit-base-patch16-224-finetuned-flower	jon-fernandes	2022-10-04T05:00:26Z	216	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-04T04:51:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/liminal-spaces-2-0	sd-concepts-library	2022-10-04T04:01:31Z	0	4	null	[ "license:mit", "region:us" ]	null	2022-10-04T04:01:19Z	--- license: mit --- ### Liminal spaces 2.0 on Stable Diffusion This is the `liminal image` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![liminal image 0](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/2.jpeg) ![liminal image 1](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/4.jpeg) ![liminal image 2](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/11.jpeg) ![liminal image 3](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/15.jpeg) ![liminal image 4](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/16.jpeg) ![liminal image 5](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/1.jpeg) ![liminal image 6](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/9.jpeg) ![liminal image 7](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/8.jpeg) ![liminal image 8](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/14.jpeg) ![liminal image 9](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/3.jpeg) ![liminal image 10](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/0.jpeg) ![liminal image 11](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/19.jpeg) ![liminal image 12](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/7.jpeg) ![liminal image 13](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/13.jpeg) ![liminal image 14](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/17.jpeg) ![liminal image 15](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/18.jpeg) ![liminal image 16](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/5.jpeg) ![liminal image 17](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/12.jpeg) ![liminal image 18](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/10.jpeg) ![liminal image 19](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/6.jpeg)
nousr/alien-diffusion	nousr	2022-10-04T03:47:20Z	0	2	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-10-04T03:44:58Z	--- license: creativeml-openrail-m ---
huggingtweets/whoisaddison	huggingtweets	2022-10-04T03:10:08Z	96	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-05-16T23:20:50Z	--- 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/1570620381324578816/UG-qT7hg_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">Addison Rae</div> <div style="text-align: center; font-size: 14px;">@whoisaddison</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 Addison Rae. \| Data \| Addison Rae \| \| --- \| --- \| \| Tweets downloaded \| 3204 \| \| Retweets \| 473 \| \| Short tweets \| 957 \| \| Tweets kept \| 1774 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6p4jofae/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 @whoisaddison's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2/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/whoisaddison') 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)
GItaf/gpt2-gpt2-mc-weight0.25-epoch15	GItaf	2022-10-04T02:57:49Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-03T14:42:06Z	--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight0.25-epoch15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-gpt2-mc-weight0.25-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8155 - Cls loss: 3.4105 - Lm loss: 3.9623 - Cls Accuracy: 0.6104 - Cls F1: 0.6054 - Cls Precision: 0.6110 - Cls Recall: 0.6104 - Perplexity: 52.58 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 4.6818 \| 1.0 \| 3470 \| 4.4437 \| 1.6250 \| 4.0374 \| 0.5274 \| 0.4958 \| 0.5329 \| 0.5274 \| 56.68 \| \| 4.3828 \| 2.0 \| 6940 \| 4.3628 \| 1.4528 \| 3.9994 \| 0.6144 \| 0.6088 \| 0.6345 \| 0.6144 \| 54.56 \| \| 4.2523 \| 3.0 \| 10410 \| 4.3820 \| 1.5899 \| 3.9842 \| 0.6092 \| 0.6025 \| 0.6382 \| 0.6092 \| 53.74 \| \| 4.1442 \| 4.0 \| 13880 \| 4.3954 \| 1.6755 \| 3.9763 \| 0.6063 \| 0.6010 \| 0.6121 \| 0.6063 \| 53.32 \| \| 4.0385 \| 5.0 \| 17350 \| 4.4675 \| 2.0051 \| 3.9659 \| 0.6150 \| 0.6105 \| 0.6194 \| 0.6150 \| 52.77 \| \| 3.9513 \| 6.0 \| 20820 \| 4.5223 \| 2.2257 \| 3.9654 \| 0.6115 \| 0.6049 \| 0.6233 \| 0.6115 \| 52.74 \| \| 3.8877 \| 7.0 \| 24290 \| 4.5904 \| 2.5003 \| 3.9649 \| 0.6012 \| 0.5956 \| 0.6049 \| 0.6012 \| 52.71 \| \| 3.8367 \| 8.0 \| 27760 \| 4.6320 \| 2.6812 \| 3.9612 \| 0.6121 \| 0.6061 \| 0.6154 \| 0.6121 \| 52.52 \| \| 3.7991 \| 9.0 \| 31230 \| 4.6735 \| 2.8534 \| 3.9596 \| 0.6104 \| 0.6059 \| 0.6139 \| 0.6104 \| 52.44 \| \| 3.7697 \| 10.0 \| 34700 \| 4.7126 \| 3.0044 \| 3.9610 \| 0.6104 \| 0.6063 \| 0.6122 \| 0.6104 \| 52.51 \| \| 3.7457 \| 11.0 \| 38170 \| 4.7607 \| 3.1961 \| 3.9612 \| 0.6133 \| 0.6072 \| 0.6182 \| 0.6133 \| 52.52 \| \| 3.7265 \| 12.0 \| 41640 \| 4.7927 \| 3.3216 \| 3.9617 \| 0.6006 \| 0.5951 \| 0.6036 \| 0.6006 \| 52.55 \| \| 3.7129 \| 13.0 \| 45110 \| 4.7983 \| 3.3431 \| 3.9620 \| 0.6104 \| 0.6039 \| 0.6133 \| 0.6104 \| 52.56 \| \| 3.7016 \| 14.0 \| 48580 \| 4.8061 \| 3.3774 \| 3.9612 \| 0.6121 \| 0.6059 \| 0.6124 \| 0.6121 \| 52.52 \| \| 3.6956 \| 15.0 \| 52050 \| 4.8155 \| 3.4105 \| 3.9623 \| 0.6104 \| 0.6054 \| 0.6110 \| 0.6104 \| 52.58 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/gpt2-gpt2-mc-weight1-epoch15	GItaf	2022-10-04T02:56:34Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-02T15:10:31Z	--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight1-epoch15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-gpt2-mc-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.6876 - Cls loss: 3.7214 - Lm loss: 3.9640 - Cls Accuracy: 0.6040 - Cls F1: 0.5981 - Cls Precision: 0.6050 - Cls Recall: 0.6040 - Perplexity: 52.67 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 6.3006 \| 1.0 \| 3470 \| 5.7550 \| 1.7137 \| 4.0417 \| 0.5326 \| 0.4990 \| 0.4983 \| 0.5326 \| 56.92 \| \| 5.5103 \| 2.0 \| 6940 \| 5.5608 \| 1.5450 \| 4.0149 \| 0.6075 \| 0.6009 \| 0.6160 \| 0.6075 \| 55.42 \| \| 5.2167 \| 3.0 \| 10410 \| 5.7608 \| 1.7609 \| 3.9988 \| 0.5977 \| 0.5917 \| 0.6161 \| 0.5977 \| 54.53 \| \| 4.9916 \| 4.0 \| 13880 \| 5.8042 \| 1.8106 \| 3.9925 \| 0.6035 \| 0.5979 \| 0.6063 \| 0.6035 \| 54.19 \| \| 4.7224 \| 5.0 \| 17350 \| 6.0519 \| 2.0699 \| 3.9807 \| 0.6144 \| 0.6100 \| 0.6152 \| 0.6144 \| 53.56 \| \| 4.4802 \| 6.0 \| 20820 \| 6.3862 \| 2.4050 \| 3.9798 \| 0.5948 \| 0.5883 \| 0.6071 \| 0.5948 \| 53.51 \| \| 4.2926 \| 7.0 \| 24290 \| 6.5793 \| 2.6045 \| 3.9733 \| 0.5890 \| 0.5819 \| 0.5940 \| 0.5890 \| 53.16 \| \| 4.1321 \| 8.0 \| 27760 \| 6.8574 \| 2.8865 \| 3.9692 \| 0.5977 \| 0.5937 \| 0.6047 \| 0.5977 \| 52.94 \| \| 4.022 \| 9.0 \| 31230 \| 7.1316 \| 3.1624 \| 3.9673 \| 0.5948 \| 0.5882 \| 0.5980 \| 0.5948 \| 52.84 \| \| 3.9255 \| 10.0 \| 34700 \| 7.1732 \| 3.2049 \| 3.9664 \| 0.6017 \| 0.5985 \| 0.6009 \| 0.6017 \| 52.79 \| \| 3.8619 \| 11.0 \| 38170 \| 7.3778 \| 3.4104 \| 3.9653 \| 0.5994 \| 0.5929 \| 0.5994 \| 0.5994 \| 52.74 \| \| 3.8141 \| 12.0 \| 41640 \| 7.5111 \| 3.5452 \| 3.9638 \| 0.5873 \| 0.5834 \| 0.5916 \| 0.5873 \| 52.66 \| \| 3.7859 \| 13.0 \| 45110 \| 7.6660 \| 3.6998 \| 3.9640 \| 0.5960 \| 0.5889 \| 0.5976 \| 0.5960 \| 52.67 \| \| 3.7628 \| 14.0 \| 48580 \| 7.6558 \| 3.6900 \| 3.9636 \| 0.5954 \| 0.5899 \| 0.5969 \| 0.5954 \| 52.65 \| \| 3.7539 \| 15.0 \| 52050 \| 7.6876 \| 3.7214 \| 3.9640 \| 0.6040 \| 0.5981 \| 0.6050 \| 0.6040 \| 52.67 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
sd-concepts-library/chungus-poodl-pet	sd-concepts-library	2022-10-04T02:56:02Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-10-04T02:55:50Z	--- license: mit --- ### Chungus Poodl Pet on Stable Diffusion This is the `<poodl-chungus-big>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<poodl-chungus-big> 0](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/52.jpeg) ![<poodl-chungus-big> 1](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/55.jpeg) ![<poodl-chungus-big> 2](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/44.jpeg) ![<poodl-chungus-big> 3](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/2.jpeg) ![<poodl-chungus-big> 4](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/27.jpeg) ![<poodl-chungus-big> 5](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/43.jpeg) ![<poodl-chungus-big> 6](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/47.jpeg) ![<poodl-chungus-big> 7](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/4.jpeg) ![<poodl-chungus-big> 8](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/32.jpeg) ![<poodl-chungus-big> 9](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/46.jpeg) ![<poodl-chungus-big> 10](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/11.jpeg) ![<poodl-chungus-big> 11](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/45.jpeg) ![<poodl-chungus-big> 12](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/15.jpeg) ![<poodl-chungus-big> 13](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/16.jpeg) ![<poodl-chungus-big> 14](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/23.jpeg) ![<poodl-chungus-big> 15](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/1.jpeg) ![<poodl-chungus-big> 16](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/9.jpeg) ![<poodl-chungus-big> 17](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/36.jpeg) ![<poodl-chungus-big> 18](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/39.jpeg) ![<poodl-chungus-big> 19](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/40.jpeg) ![<poodl-chungus-big> 20](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/26.jpeg) ![<poodl-chungus-big> 21](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/8.jpeg) ![<poodl-chungus-big> 22](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/38.jpeg) ![<poodl-chungus-big> 23](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/42.jpeg) ![<poodl-chungus-big> 24](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/22.jpeg) ![<poodl-chungus-big> 25](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/35.jpeg) ![<poodl-chungus-big> 26](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/33.jpeg) ![<poodl-chungus-big> 27](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/49.jpeg) ![<poodl-chungus-big> 28](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/14.jpeg) ![<poodl-chungus-big> 29](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/3.jpeg) ![<poodl-chungus-big> 30](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/51.jpeg) ![<poodl-chungus-big> 31](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/37.jpeg) ![<poodl-chungus-big> 32](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/30.jpeg) ![<poodl-chungus-big> 33](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/0.jpeg) ![<poodl-chungus-big> 34](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/19.jpeg) ![<poodl-chungus-big> 35](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/54.jpeg) ![<poodl-chungus-big> 36](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/24.jpeg) ![<poodl-chungus-big> 37](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/7.jpeg) ![<poodl-chungus-big> 38](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/48.jpeg) ![<poodl-chungus-big> 39](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/21.jpeg) ![<poodl-chungus-big> 40](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/50.jpeg) ![<poodl-chungus-big> 41](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/25.jpeg) ![<poodl-chungus-big> 42](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/13.jpeg) ![<poodl-chungus-big> 43](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/17.jpeg) ![<poodl-chungus-big> 44](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/31.jpeg) ![<poodl-chungus-big> 45](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/18.jpeg) ![<poodl-chungus-big> 46](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/5.jpeg) ![<poodl-chungus-big> 47](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/29.jpeg) ![<poodl-chungus-big> 48](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/34.jpeg) ![<poodl-chungus-big> 49](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/28.jpeg) ![<poodl-chungus-big> 50](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/41.jpeg) ![<poodl-chungus-big> 51](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/12.jpeg) ![<poodl-chungus-big> 52](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/20.jpeg) ![<poodl-chungus-big> 53](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/10.jpeg) ![<poodl-chungus-big> 54](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/53.jpeg) ![<poodl-chungus-big> 55](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/6.jpeg)
anas-awadalla/gpt2-large-span-head-finetuned-squad	anas-awadalla	2022-10-04T01:27:34Z	537	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2022-09-26T20:01:28Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-large-span-head-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large-span-head-finetuned-squad This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the squad 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
aaronryanmanuel/distilbert-base-uncased-finetuned-cola	aaronryanmanuel	2022-10-04T01:21:22Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T15:38:32Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5229395497643199 --- <!-- 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. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8233 - Matthews Correlation: 0.5229 ## 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: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5367 \| 1.0 \| 535 \| 0.5657 \| 0.3638 \| \| 0.3692 \| 2.0 \| 1070 \| 0.5291 \| 0.4912 \| \| 0.2503 \| 3.0 \| 1605 \| 0.5442 \| 0.5038 \| \| 0.1895 \| 4.0 \| 2140 \| 0.7376 \| 0.5112 \| \| 0.1363 \| 5.0 \| 2675 \| 0.8233 \| 0.5229 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
waifu-research-department/Zero-Two	waifu-research-department	2022-10-04T00:21:52Z	0	3	null	[ "region:us" ]	null	2022-10-03T01:35:48Z	# Description Trainer: naotsue Zero Two from Darling in the FranXX # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: ZERO-TWO (Use this in the prompt) >Class Phrase: 1girl ![Sak](https://www.enjpg.com/img/2020/zero-two-57.jpg)
waifu-research-department/Ayanokouji-Kiyotaka	waifu-research-department	2022-10-04T00:20:41Z	0	2	null	[ "region:us" ]	null	2022-10-03T01:35:12Z	# Description Trainer: naotsue Ayanokouji Kiyotaka from Classroom of the Elite # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: AYANOKOUJI (Use this in the prompt) >Class Phrase: 1boy ![Sak](https://animevania.com/wp-content/uploads/2022/07/classroom-of-the-elite-kiyotaka-ayanokoji-4k-wallpaper-uhdpaper.com-557@[email protected])
grantsl/distilbert-base-uncased-finetuned-emotion-2	grantsl	2022-10-04T00:16:54Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T23:32:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.8433 - F1: 0.8433 ## 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: 128 - eval_batch_size: 128 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4095 \| 1.0 \| 875 \| 0.3667 \| 0.8353 \| 0.8351 \| \| 0.3348 \| 2.0 \| 1750 \| 0.3608 \| 0.8433 \| 0.8433 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
MoseliMotsoehli/FishNet	MoseliMotsoehli	2022-10-03T23:30:15Z	0	1	null	[ "license:openrail", "region:us" ]	null	2022-10-02T20:37:48Z	--- license: openrail --- # <span style="color:blue">FishNet: AI For Fish Stock Estimation</span> The attached model was trained on a 63000 dataset of fish images belonging to 163 species. First, we trained a detectron2 model to detect and segment fish and fiduciary markers on a board. The detectron2 model was written in PyTorch, and the final classifier is a ResNet50 keras model. Below is an example of how to use the 2 models. ## Packages ## Load Models ## load an image and transform ## run the segmentation model ## visualize ## classify the fish
faisito/distilbert-base-uncased-finetuned-emotion	faisito	2022-10-03T22:19:00Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T21:45:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.925520268497019 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9255 - F1: 0.9255 ## 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: 64 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8237 \| 1.0 \| 250 \| 0.3205 \| 0.9045 \| 0.9002 \| \| 0.2539 \| 2.0 \| 500 \| 0.2170 \| 0.9255 \| 0.9255 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
suresh-subramanian/autotrain-fake-news-1649058542	suresh-subramanian	2022-10-03T22:13:59Z	103	0	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T22:08:00Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 12.699762619910537 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058542 - CO2 Emissions (in grams): 12.6998 ## Validation Metrics - Loss: 0.624 - Accuracy: 0.637 - Precision: 1.000 - Recall: 0.020 - AUC: 0.652 - F1: 0.039 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058542 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
suresh-subramanian/autotrain-fake-news-1649058539	suresh-subramanian	2022-10-03T22:12:00Z	102	0	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T22:07:19Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 0.040297872306469855 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058539 - CO2 Emissions (in grams): 0.0403 ## Validation Metrics - Loss: 0.478 - Accuracy: 0.779 - Precision: 0.814 - Recall: 0.520 - AUC: 0.881 - F1: 0.635 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058539 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
suresh-subramanian/autotrain-fake-news-1649058538	suresh-subramanian	2022-10-03T22:11:11Z	100	0	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T22:07:12Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 0.04097854185629584 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058538 - CO2 Emissions (in grams): 0.0410 ## Validation Metrics - Loss: 0.387 - Accuracy: 0.815 - Precision: 0.760 - Recall: 0.730 - AUC: 0.902 - F1: 0.745 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058538 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
allenai/scibert_scivocab_uncased	allenai	2022-10-03T22:06:12Z	691,053	137	transformers	[ "transformers", "pytorch", "jax", "bert", "en", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en --- # SciBERT This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. Available models include: * `scibert_scivocab_cased` * `scibert_scivocab_uncased` The original repo can be found [here](https://github.com/allenai/scibert). If using these models, please cite the following paper: ``` @inproceedings{beltagy-etal-2019-scibert, title = "SciBERT: A Pretrained Language Model for Scientific Text", author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", booktitle = "EMNLP", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1371" } ```
allenai/scibert_scivocab_cased	allenai	2022-10-03T22:04:46Z	6,859	14	transformers	[ "transformers", "pytorch", "jax", "bert", "en", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en --- # SciBERT This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. Available models include: * `scibert_scivocab_cased` * `scibert_scivocab_uncased` The original repo can be found [here](https://github.com/allenai/scibert). If using these models, please cite the following paper: ``` @inproceedings{beltagy-etal-2019-scibert, title = "SciBERT: A Pretrained Language Model for Scientific Text", author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", booktitle = "EMNLP", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1371" } ```
allenai/aspire-biencoder-compsci-spec	allenai	2022-10-03T22:03:49Z	116	1	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-04-23T14:15:21Z	--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" Authors: Sheshera Mysore, Arman Cohan, Tom Hope Paper: https://arxiv.org/abs/2111.08366 Github: https://github.com/allenai/aspire Note: In the context of the paper, this model is referred to as `Specter-CoCite_Spec` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SPECTER model. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the base encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-compsci-spec-full.zip`](https://drive.google.com/file/d/1AHtzyEpyn7DeFYOdt86ik4n0tGaG5kMC/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in computer science scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from computer science, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Performance here is reported on CSFCube (computer science/English). This is detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). CSFCube presents a finer-grained query via selected sentences in a query abstract based on which a finer-grained retrieval must be made from candidate abstracts. The bi-encoder above ignores the finer grained query sentences and uses the whole abstract - this presents a baseline in the paper. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-compsci-spec` (and `aspire-biencoder-compsci-spec-full`) is compared against `allenai/specter`. `aspire-biencoder-compsci-spec-full`<sup></sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-compsci-spec` and `aspire-biencoder-compsci-spec-full` are the single best run among the 3 re-runs. \| \| CSFCube aggregated \| CSFCube aggregated\| \|--------------------------------------------:\|:---------:\|:-------:\| \| \| MAP \| NDCG%20 \| \| `specter` \| 34.23 \| 53.28 \| \| `aspire-biencoder-compsci-spec-full`<sup></sup> \| 37.90 \| 58.16 \| \| `aspire-biencoder-compsci-spec` \| 37.17 \| 57.91 \| \| `aspire-biencoder-compsci-spec-full` \| 37.67 \| 59.26 \| Alternative models: Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-biomed-scib`](https://huggingface.co/allenai/aspire-biencoder-biomed-scib): If you wanted to run on biomedical papers.
allenai/aspire-biencoder-biomed-spec	allenai	2022-10-03T22:03:21Z	117	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-04-23T14:14:35Z	--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" Authors: Sheshera Mysore, Arman Cohan, Tom Hope Paper: https://arxiv.org/abs/2111.08366 Github: https://github.com/allenai/aspire Note: In the context of the paper, this model is referred to as `Specter-CoCite_Spec` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SPECTER encoder. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the base encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-spec-full.zip`](https://drive.google.com/file/d/1MDCv9Fc33eP015HTWKi50WYXixh72h5c/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 1e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in biomedical scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-spec` (and `aspire-biencoder-biomed-spec-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-spec-full`<sup></sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-spec` and `aspire-biencoder-biomed-spec-full` are the single best run among the 3 re-runs. \| \| TRECCOVID \| TRECCOVID \| RELISH \| RELISH \| \|-------------------------------------------:\|:---------:\|:-------:\|:------:\|:-------:\| \| \| MAP \| NDCG%20 \| MAP \| NDCG%20 \| \| `specter` \| 28.24 \| 59.28 \| 60.62\| 77.20 \| \| `aspire-biencoder-biomed-spec-full`<sup></sup> \| 28.59 \| 60.07 \| 61.43\| 77.96 \| \| `aspire-biencoder-biomed-spec` \| 26.07 \| 54.89 \| 61.47\| 78.34 \| \| `aspire-biencoder-biomed-spec-full` \| 28.87 \| 60.47 \| 61.69\| 78.22 \| Note that the absence of linear mixing parameters in the `aspire-biencoder-biomed-spec` hurts performance substantially compared to `aspire-biencoder-biomed-spec-full` in TRECCOVID - this dataset contains a larger candidate set than RELISH (~9000 vs 60). Consider the more performant Alternative models below for usage. Alternative models: Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-scib`](https://huggingface.co/allenai/aspire-biencoder-biomed-scib): This is an alternative bi-encoder model identical to the above model, except that it is initialized with SciBERT instead of SPECTER. The above model underperforms this model, `allenai/aspire-biencoder-biomed-scib` (even better, `aspire-biencoder-biomed-scib-full`) is recommended for use.
anas-awadalla/gpt2-span-head-finetuned-squad	anas-awadalla	2022-10-03T21:37:18Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2022-10-03T13:59:42Z	--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-span-head-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-span-head-finetuned-squad This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the squad 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: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
allenai/longformer-base-4096-extra.pos.embd.only	allenai	2022-10-03T21:21:08Z	341	1	transformers	[ "transformers", "pytorch", "tf", "longformer", "en", "arxiv:2004.05150", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: en --- # longformer-base-4096-extra.pos.embd.only This model is similar to `longformer-base-4096` but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings. ### Citing If you use `Longformer` in your research, please cite [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150). ``` @article{Beltagy2020Longformer, title={Longformer: The Long-Document Transformer}, author={Iz Beltagy and Matthew E. Peters and Arman Cohan}, journal={arXiv:2004.05150}, year={2020}, } ``` `Longformer` is an open-source project developed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.
ryyyyyy/1	ryyyyyy	2022-10-03T21:21:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-10-03T21:21:00Z	--- license: creativeml-openrail-m ---
Hitmewmusic/First	Hitmewmusic	2022-10-03T21:20:52Z	0	0	null	[ "region:us" ]	null	2022-10-03T21:20:38Z	git clone https://github.com/sd-webui/stable-diffusion-webui.git
allenai/bidaf-elmo	allenai	2022-10-03T21:18:47Z	27	12	allennlp	[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: en tags: - allennlp - question-answering --- This is an implementation of the BiDAF model with ELMo embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word representations (this is the only part that is at all non-standard), pass this through another few layers of bi-LSTMs/GRUs, and do a softmax over span start and span end. CAVEATS: ------ This model is based on ELMo. ELMo is not deterministic, meaning that you will see slight differences every time you run it. Also, ELMo likes to be warmed up, so we recommend processing dummy input before processing real workloads with it.
allenai/aspire-contextualsentence-multim-biomed	allenai	2022-10-03T21:17:27Z	132	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-04-23T14:15:56Z	--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" Authors: Sheshera Mysore, Arman Cohan, Tom Hope Paper: https://arxiv.org/abs/2111.08366 Github: https://github.com/allenai/aspire Note: In the context of the paper, this model is referred to as `tsAspire` and represents the papers proposed multi-vector model for fine-grained scientific document similarity. ## Model Card ### Model description This model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - the whole title and abstract are encoded with cross-attention in the encoder block before obtaining sentence embeddings. The model is trained by minimizing an Wasserstein/Earth Movers Distance between sentence vectors for a pair of documents - in the process also learning a sparse alignment between sentences in both documents. Test time behavior ranks documents based on the Wasserstein Distance between all sentences of documents or a set of query sentences and a candidate documents sentences. ### Training data The model is trained on pairs of co-cited papers with their sentences aligned by the co-citation context in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model, negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers. For example - the papers in brackets below are all co-cited and each pair of papers would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for fine-grained document similarity tasks in biomedical scientific text using multiple vectors per document. The model allows _multiple_ fine grained sentence-to-sentence similarities between documents. The model is well suited to an aspect conditional task formulation where a query might consist of sentence_s_ in a query document and candidates must be retrieved along the specified sentences. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as document or sentence level classification. Since the training data comes primarily from biomedical, performance on other domains may be poorer. ### How to use This model can be used via the `transformers` library, and some additional code to compute contextual sentence vectors and to make multiple matches using optimal transport. View example usage and sample document matches in the model github repo: [`examples/demo-contextualsentence-multim.ipynb`](https://github.com/allenai/aspire/blob/main/examples/demo-contextualsentence-multim.ipynb) ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. In using this model we rank documents by the Wasserstein distance between the query sentences and a candidates sentences. ### Evaluation results The released model `aspire-contextualsentence-multim-biomed` is compared against `allenai/specter`. `aspire-contextualsentence-multim-biomed`<sup></sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-contextualsentence-multim-biomed` is the single best run among the 3 re-runs. \| \| TRECCOVID \| TRECCOVID \| RELISH \| RELISH \| \|-------------------------------------------:\|:---------:\|:-------:\|:------:\|:-------:\| \| \| MAP \| NDCG%20 \| MAP \| NDCG%20 \| \| `specter` \| 28.24 \| 59.28 \| 60.62 \| 77.20 \| \| `aspire-contextualsentence-multim-biomed`<sup></sup> \| 30.92 \| 62.23 \| 62.57 \| 78.95 \| \| `aspire-contextualsentence-multim-biomed` \| 31.25 \| 62.99 \| 62.24 \| 78.65 \| Alternative models: Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-contextualsentence-multim-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-multim-compsci): If you wanted to run on computer science papers and want to use a model trained to match _multiple_ sentences between documents. [`aspire-contextualsentence-singlem-biomed`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-biomed): If you wanted to run on biomedical papers and want to use a model trained to match _single_ sentences between documents. [`aspire-contextualsentence-singlem-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-compsci): If you wanted to run on computer science papers and want to use a model trained to match _single_ sentences between documents.
allenai/aspire-biencoder-biomed-scib	allenai	2022-10-03T21:12:46Z	122	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-04-23T13:47:04Z	--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" Authors: Sheshera Mysore, Arman Cohan, Tom Hope Paper: https://arxiv.org/abs/2111.08366 Github: https://github.com/allenai/aspire Note: In the context of the paper, this model is referred to as `Specter-CoCite_Scib` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is initialized with the SciBert model. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the SciBert encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in biomedical scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-scib` (and `aspire-biencoder-biomed-scib-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-scib-full`<sup></sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-scib` and `aspire-biencoder-biomed-scib-full` are the single best run among the 3 re-runs. \| \| TRECCOVID \| TRECCOVID \| RELISH \| RELISH \| \|-------------------------------------------:\|:---------:\|:-------:\|:------:\|:-------:\| \| \| MAP \| NDCG%20 \| MAP \| NDCG%20 \| \| `specter` \| 28.24 \| 59.28 \| 60.62\| 77.20 \| \| `aspire-biencoder-biomed-scib-full`<sup></sup> \| 30.60 \| 62.07 \| 61.43\| 78.01 \| \| `aspire-biencoder-biomed-scib` \| 30.74 \| 60.16 \| 61.52\| 78.07 \| \| `aspire-biencoder-biomed-scib-full` \| 31.45 \| 63.15 \| 61.34\| 77.89 \| Alternative models: Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-spec`](https://huggingface.co/allenai/aspire-biencoder-biomed-spec): This is an alternative bi-encoder model identical to the above model, except that it is initialized with `allenai/specter` instead of SciBert. This usually under-performs the model released here.
allenai/naqanet	allenai	2022-10-03T21:12:40Z	19	2	allennlp	[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: en tags: - allennlp - question-answering widget: - context: 'A reusable launch system (RLS, or reusable launch vehicle, RLV) is a launch system which is capable of launching a payload into space more than once. This contrasts with expendable launch systems, where each launch vehicle is launched once and then discarded. No completely reusable orbital launch system has ever been created. Two partially reusable launch systems were developed, the Space Shuttle and Falcon 9. The Space Shuttle was partially reusable: the orbiter (which included the Space Shuttle main engines and the Orbital Maneuvering System engines), and the two solid rocket boosters were reused after several months of refitting work for each launch. The external tank was discarded after each flight.' text: How many partially reusable launch systems were developed? example_title: Reusable launch systems - context: Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing. These technologies are used to develop machines that can substitute for humans. Robots can be used in any situation and for any purpose, but today many are used in dangerous environments (including bomb detection and de-activation), manufacturing processes, or where humans cannot survive. Robots can take on any form but some are made to resemble humans in appearance. This is said to help in the acceptance of a robot in certain replicative behaviors usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, and basically anything a human can do. text: What do robots that resemble humans attempt to do? example_title: Robots - context: In the first quarter, the Bears drew first blood as kicker Robbie Gould nailed a 22-yard field goal for the only score of the period. In the second quarter, the Bears increased their lead with Gould nailing a 42-yard field goal. They increased their lead with Cutler firing a 7-yard TD pass to tight end Greg Olsen. The Bears then closed out the first half with Gould's 41-yard field goal. In the third quarter, the Vikes started to rally with running back Adrian Peterson's 1-yard touchdown run (with the extra point attempt blocked). The Bears increased their lead over the Vikings with Cutler's 2-yard TD pass to tight end Desmond Clark. The Vikings then closed out the quarter with quarterback Brett Favre firing a 6-yard TD pass to tight end Visanthe Shiancoe. An exciting fourth quarter ensued. The Vikings started out the quarter's scoring with kicker Ryan Longwell's 41-yard field goal, along with Adrian Peterson's second 1-yard TD run. The Bears then responded with Cutler firing a 20-yard TD pass to wide receiver Earl Bennett. The Vikings then completed the remarkable comeback with Favre finding wide receiver Sidney Rice on a 6-yard TD pass on 4th-and-goal with 15 seconds left in regulation. The Bears then took a knee to force overtime. In overtime, the Bears won the toss and marched down the field, stopping at the 35-yard line. However, the potential game-winning 45-yard field goal attempt by Gould went wide right, giving the Vikings a chance to win. After an exchange of punts, the Vikings had the ball at the 26-yard line with 11 minutes left in the period. On the first play of scrimmage, Favre fired a screen pass to Peterson who caught it and went 16 yards, before being confronted by Hunter Hillenmeyer, who caused Peterson to fumble the ball, which was then recovered by Bears' linebacker Nick Roach. The Bears then won on Jay Cutler's game-winning 39-yard TD pass to wide receiver Devin Aromashodu. With the loss, not only did the Vikings fall to 11-4, they also surrendered homefield advantage to the Saints. text: Who threw the longest touchdown pass of the game? example_title: Argmax - context: Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens. The Texans would respond with fullback Vonta Leach getting a 1-yard touchdown run, yet the Raiders would answer with kicker Sebastian Janikowski getting a 33-yard and a 30-yard field goal. Houston would tie the game in the second quarter with kicker Kris Brown getting a 53-yard and a 24-yard field goal. Oakland would take the lead in the third quarter with wide receiver Johnnie Lee Higgins catching a 29-yard touchdown pass from Russell, followed up by an 80-yard punt return for a touchdown. The Texans tried to rally in the fourth quarter as Brown nailed a 40-yard field goal, yet the Raiders' defense would shut down any possible attempt. text: How many yards was the longest passing touchdown? example_title: Max - context: In 1085, Guadalajara was retaken by the Christian forces of Alfonso VI . The chronicles say that the Christian army was led by Alvar Fanez de Minaya, one of the lieutenants of El Cid. From 1085 until the Battle of Las Navas de Tolosa in 1212, the city suffered wars against the Almoravid and the Almohad Empires. In spite of the wars, the Christian population could definitely settle down in the area thanks to the repopulation with people from the North who received their first fuero in 1133 from Alfonso VII.In 1219, the king Fernando III gave a new fuero to the city .During the reign of Alfonso X of Castile, the protection of the king allowed the city to develop its economy by protecting merchants and allowing markets. text: How many years did the city suffer wars against Almoravid and the Almohad Empires? example_title: Arithmetic --- An augmented version of QANet that adds rudimentary numerical reasoning ability, trained on DROP (Dua et al., 2019), as published in the original DROP paper. An augmented version of QANet model with some rudimentary numerical reasoning abilities. The main idea here is that instead of just predicting a passage span after doing all of the QANet modeling stuff, we add several different ‘answer abilities’: predicting a span from the question, predicting a count, or predicting an arithmetic expression. Near the end of the QANet model, we have a variable that predicts what kind of answer type we need, and each branch has separate modeling logic to predict that answer type. We then marginalize over all possible ways of getting to the right answer through each of these answer types.
allenai/transformer_qa	allenai	2022-10-03T21:12:21Z	12	3	allennlp	[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: en tags: - allennlp - question-answering --- A reading comprehension model patterned after the proposed model in Devlin et al, with improvements borrowed from the SQuAD model in the transformers project The model implements a reading comprehension model patterned after the proposed model in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al, 2018), with improvements borrowed from the SQuAD model in the transformers project. It predicts start tokens and end tokens with a linear layer on top of word piece embeddings.
misterkilgore/distilbert-base-uncased-finetuned-disaster-tweet	misterkilgore	2022-10-03T20:09:45Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-30T12:51:56Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-disaster-tweet results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-disaster-tweet This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4052 - Accuracy: 0.8207 - F1: 0.8203 ## 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: 64 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5056 \| 1.0 \| 96 \| 0.4139 \| 0.8188 \| 0.8179 \| \| 0.3991 \| 2.0 \| 192 \| 0.4052 \| 0.8207 \| 0.8203 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Arklyn/fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1	Arklyn	2022-10-03T20:00:31Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_10_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-03T07:36:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_10_0 model-index: - name: fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2114 - Wer: 0.1762 ## 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: 0.0001 - train_batch_size: 36 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.9372 \| 4.0 \| 460 \| 2.8359 \| 1.0 \| \| 2.2755 \| 8.0 \| 920 \| 0.3708 \| 0.3983 \| \| 0.6614 \| 12.0 \| 1380 \| 0.2433 \| 0.2636 \| \| 0.5071 \| 16.0 \| 1840 \| 0.2347 \| 0.2395 \| \| 0.4516 \| 20.0 \| 2300 \| 0.2213 \| 0.2185 \| \| 0.4206 \| 24.0 \| 2760 \| 0.2222 \| 0.2008 \| \| 0.3844 \| 28.0 \| 3220 \| 0.2072 \| 0.1887 \| \| 0.3678 \| 32.0 \| 3680 \| 0.2071 \| 0.1886 \| \| 0.3565 \| 36.0 \| 4140 \| 0.2015 \| 0.1851 \| \| 0.3388 \| 40.0 \| 4600 \| 0.2137 \| 0.1850 \| \| 0.3235 \| 44.0 \| 5060 \| 0.2072 \| 0.1791 \| \| 0.3173 \| 48.0 \| 5520 \| 0.2095 \| 0.1777 \| \| 0.3088 \| 52.0 \| 5980 \| 0.2102 \| 0.1784 \| \| 0.3 \| 56.0 \| 6440 \| 0.2164 \| 0.1772 \| \| 0.2957 \| 60.0 \| 6900 \| 0.2114 \| 0.1762 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.10.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
neelrr/xlm-roberta-base-finetuned-panx-ta	neelrr	2022-10-03T18:57:36Z	126	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-03T18:52:53Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-ta results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.ta metrics: - name: F1 type: f1 value: 0.8144578313253013 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-ta This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - F1: 0.8145 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5477 \| 1.0 \| 209 \| 0.2732 \| 0.7305 \| \| 0.2506 \| 2.0 \| 418 \| 0.2425 \| 0.7626 \| \| 0.168 \| 3.0 \| 627 \| 0.2183 \| 0.8145 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
neelrr/xlm-roberta-base-finetuned-panx-hi-mr	neelrr	2022-10-03T18:44:54Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-03T18:37:10Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-hi-mr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-hi-mr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1942 - F1: 0.8710 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4628 \| 1.0 \| 417 \| 0.2603 \| 0.8062 \| \| 0.2064 \| 2.0 \| 834 \| 0.1951 \| 0.8492 \| \| 0.1289 \| 3.0 \| 1251 \| 0.1942 \| 0.8710 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
jinhybr/text-summarization-t5base-xsum	jinhybr	2022-10-03T18:42:32Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-29T13:58:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 28.282 --- <!-- 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 xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4789 - Rouge1: 28.282 - Rouge2: 7.6989 - Rougel: 22.2019 - Rougelsum: 22.197 - Gen Len: 18.8238 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.7189 \| 1.0 \| 12753 \| 2.4789 \| 28.282 \| 7.6989 \| 22.2019 \| 22.197 \| 18.8238 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
albertdestajo/distilbert-base-uncased-finetuned-sst2	albertdestajo	2022-10-03T18:39:23Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-27T20:09:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: train args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9025229357798165 --- <!-- 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. --> # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2991 - Accuracy: 0.9025 ## 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: 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.227 \| 1.0 \| 4210 \| 0.2991 \| 0.9025 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
radhe2205/finetuning-sentiment-model-3000-samples	radhe2205	2022-10-03T18:34:18Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T18:25:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.6566666666666666 - name: F1 type: f1 value: 0.6979472140762463 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7339 - Accuracy: 0.6567 - F1: 0.6979 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
mofyrt/finetuning-sentiment-model-5000-samples	mofyrt	2022-10-03T18:31:16Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T18:00:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-5000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9104 - name: F1 type: f1 value: 0.9115673114883537 --- <!-- 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. --> # finetuning-sentiment-model-5000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5802 - Accuracy: 0.9104 - F1: 0.9116 ## 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: 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: 10 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
ad7/finetuning-sentiment-model-3000-samples	ad7	2022-10-03T18:18:09Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T18:08:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.77 - name: F1 type: f1 value: 0.7561837455830389 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6587 - Accuracy: 0.77 - F1: 0.7562 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
eligabel/finetuning-sentiment-model-3000-samples	eligabel	2022-10-03T18:12:07Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T18:02:03Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8166666666666667 - name: F1 type: f1 value: 0.8307692307692307 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6069 - Accuracy: 0.8167 - F1: 0.8308 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
ilex/finetuning-sentiment-model-3000-samples	ilex	2022-10-03T18:10:45Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T18:01:43Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.7533333333333333 - name: F1 type: f1 value: 0.7797619047619048 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6197 - Accuracy: 0.7533 - F1: 0.7798 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
chenyueg/finetuning-sentiment-model-3000-samples	chenyueg	2022-10-03T18:02:20Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T04:48:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.6566666666666666 - name: F1 type: f1 value: 0.6555183946488293 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6751 - Accuracy: 0.6567 - F1: 0.6555 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Bakuraza/FirstLunarLanding	Bakuraza	2022-10-03T17:16:52Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-03T17:16:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 229.59 +/- 12.98 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
model-attribution-challenge/codegen-350M-multi	model-attribution-challenge	2022-10-03T16:18:49Z	108	2	transformers	[ "transformers", "pytorch", "codegen", "text-generation", "arxiv:2203.13474", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-07-26T13:36:04Z	--- license: bsd-3-clause --- # CodeGen (CodeGen-Multi 350M) ## Model description CodeGen is a family of autoregressive language models for program synthesis from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as CodeGen-Multi 350M in the paper, where "Multi" means the model is initialized with CodeGen-NL 350M and further pre-trained on a dataset of multiple programming languages, and "350M" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Multi 350M) was firstly initialized with CodeGen-NL 350M, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at program synthesis, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```
jschmock/gewerke	jschmock	2022-10-03T15:49:04Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-27T10:30:34Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: gewerke results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gewerke This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0089 - F1: 0.9974 ## Label-Übersetzung - 0 Abwasser-Wasser-Gasanlagen - 1 Andere Anlagen - 2 Gebäudeautomation - 3 Kälteanlagen - 4 Lufttechnische Anlagen - 5 Starkstromanlagen - 6 Wärmeversorungsanlagen ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.99 \| 90 \| 0.0444 \| 0.9886 \| \| No log \| 1.99 \| 180 \| 0.0182 \| 0.9947 \| \| No log \| 2.99 \| 270 \| 0.0103 \| 0.9974 \| \| No log \| 3.99 \| 360 \| 0.0152 \| 0.9946 \| \| No log \| 4.99 \| 450 \| 0.0089 \| 0.9974 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/jang-sung-rak-style	sd-concepts-library	2022-10-03T15:43:16Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-10-03T15:43:05Z	--- license: mit --- ### Jang-Sung-Rak-Style on Stable Diffusion This is the `<Jang-Sung-Rak-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<Jang-Sung-Rak-style> 0](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/1.jpeg) ![<Jang-Sung-Rak-style> 1](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/4.jpeg) ![<Jang-Sung-Rak-style> 2](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/2.jpeg) ![<Jang-Sung-Rak-style> 3](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/0.jpeg) ![<Jang-Sung-Rak-style> 4](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/3.jpeg) ![<Jang-Sung-Rak-style> 5](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/6.jpeg) ![<Jang-Sung-Rak-style> 6](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/5.jpeg)
GItaf/gpt2-gpt2-mc-weight2-epoch15	GItaf	2022-10-03T14:40:03Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-03T06:53:43Z	--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight2-epoch15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-gpt2-mc-weight2-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.3067 - Cls loss: 3.6680 - Lm loss: 3.9663 - Cls Accuracy: 0.6069 - Cls F1: 0.6023 - Cls Precision: 0.6050 - Cls Recall: 0.6069 - Perplexity: 52.79 ## 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 8.1971 \| 1.0 \| 3470 \| 7.6018 \| 1.7777 \| 4.0471 \| 0.5251 \| 0.4892 \| 0.5282 \| 0.5251 \| 57.23 \| \| 7.0508 \| 2.0 \| 6940 \| 7.2679 \| 1.6195 \| 4.0269 \| 0.6058 \| 0.6001 \| 0.6226 \| 0.6058 \| 56.08 \| \| 6.5979 \| 3.0 \| 10410 \| 7.4637 \| 1.7253 \| 4.0112 \| 0.6184 \| 0.6109 \| 0.6367 \| 0.6184 \| 55.21 \| \| 6.1811 \| 4.0 \| 13880 \| 7.8253 \| 1.9084 \| 4.0063 \| 0.6121 \| 0.6074 \| 0.6207 \| 0.6121 \| 54.94 \| \| 5.7242 \| 5.0 \| 17350 \| 8.1576 \| 2.0824 \| 3.9903 \| 0.6219 \| 0.6166 \| 0.6195 \| 0.6219 \| 54.07 \| \| 5.2588 \| 6.0 \| 20820 \| 8.9472 \| 2.4785 \| 3.9872 \| 0.6006 \| 0.5942 \| 0.6169 \| 0.6006 \| 53.90 \| \| 4.8854 \| 7.0 \| 24290 \| 9.2399 \| 2.6264 \| 3.9840 \| 0.6063 \| 0.5988 \| 0.6123 \| 0.6063 \| 53.73 \| \| 4.5785 \| 8.0 \| 27760 \| 9.7123 \| 2.8660 \| 3.9768 \| 0.6121 \| 0.6076 \| 0.6124 \| 0.6121 \| 53.35 \| \| 4.3508 \| 9.0 \| 31230 \| 10.2550 \| 3.1400 \| 3.9712 \| 0.6086 \| 0.6017 \| 0.6121 \| 0.6086 \| 53.05 \| \| 4.1817 \| 10.0 \| 34700 \| 10.3110 \| 3.1681 \| 3.9711 \| 0.6058 \| 0.5990 \| 0.6047 \| 0.6058 \| 53.04 \| \| 4.0546 \| 11.0 \| 38170 \| 11.0526 \| 3.5396 \| 3.9693 \| 0.5988 \| 0.5929 \| 0.5997 \| 0.5988 \| 52.95 \| \| 3.9481 \| 12.0 \| 41640 \| 11.0193 \| 3.5238 \| 3.9675 \| 0.6086 \| 0.6038 \| 0.6049 \| 0.6086 \| 52.85 \| \| 3.9008 \| 13.0 \| 45110 \| 11.2499 \| 3.6394 \| 3.9669 \| 0.6121 \| 0.6073 \| 0.6090 \| 0.6121 \| 52.82 \| \| 3.8558 \| 14.0 \| 48580 \| 11.3606 \| 3.6948 \| 3.9666 \| 0.6063 \| 0.6000 \| 0.6034 \| 0.6063 \| 52.81 \| \| 3.8297 \| 15.0 \| 52050 \| 11.3067 \| 3.6680 \| 3.9663 \| 0.6069 \| 0.6023 \| 0.6050 \| 0.6069 \| 52.79 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
ironbar/stable_diffusion_gmartinez	ironbar	2022-10-03T13:50:16Z	0	0	null	[ "region:us" ]	null	2022-10-03T13:29:06Z	Stable-diffusion fine-tuned with this [repo](https://github.com/JoePenna/Dreambooth-Stable-Diffusion/) to create images from my friend Gonzalo. ![joker](https://huggingface.co/ironbar/stable_diffusion_gmartinez/blob/main/joker.jpeg)
zannabethl/opus-mt-en-ro-finetuned-en-to-ro	zannabethl	2022-10-03T13:00:35Z	116	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-03T12:22:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 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: 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: 1 ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
damilojohn/Bert2BertForTextDescrambling	damilojohn	2022-10-03T12:40:40Z	111	1	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-03T00:44:12Z	--- license: apache-2.0 --- This model receives scrambled or incoherent sentences as input and returns a meaningful sentence using the same words in the input . A form of grammar correction if you may . It was trained on a dataset of permutated sentences derived from wikipedia pages as input with the correct arrangement of words as labels . It is an encoder-decoder model that uses BERT's weight in both it's encoder and decoder .
cw1521/opus-mt-st-en	cw1521	2022-10-03T12:36:01Z	115	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-29T01:37:55Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-st-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-st-en This model is a fine-tuned version of [cw1521/opus-mt-st-en](https://huggingface.co/cw1521/opus-mt-st-en) on an unknown 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: 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: 1 ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
cw1521/opus-mt-en-st	cw1521	2022-10-03T12:31:35Z	112	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-29T02:29:39Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-st results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-st This model is a fine-tuned version of [cw1521/opus-mt-en-st](https://huggingface.co/cw1521/opus-mt-en-st) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 - Bleu: 77.1725 - Gen Len: 60.3696 ## 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: 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.3768 \| 1.0 \| 969 \| 0.3710 \| 77.1725 \| 60.3696 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/aadhav-face	sd-concepts-library	2022-10-03T12:22:50Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-10-03T12:22:47Z	--- license: mit --- ### aadhav face on Stable Diffusion This is the `<aadhav-face>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<aadhav-face> 0](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/1.jpeg) ![<aadhav-face> 1](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/2.jpeg) ![<aadhav-face> 2](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/0.jpeg) ![<aadhav-face> 3](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/3.jpeg)
Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit	Muennighoff	2022-10-03T12:16:09Z	828	6	sentence-transformers	[ "sentence-transformers", "pytorch", "gptj", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:04Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-5.8B-weightedmean-nli-bitfit results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 74.07462686567165 - type: ap value: 37.44692407529112 - type: f1 value: 68.28971003916419 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 66.63811563169165 - type: ap value: 78.57252079915924 - type: f1 value: 64.5543087846584 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 77.21889055472263 - type: ap value: 25.663426367826712 - type: f1 value: 64.26265688503176 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 58.06209850107067 - type: ap value: 14.028219107023915 - type: f1 value: 48.10387189660778 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 82.30920000000002 - type: ap value: 76.88786578621213 - type: f1 value: 82.15455656065011 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 41.584 - type: f1 value: 41.203137944390114 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.288000000000004 - type: f1 value: 34.672995558518096 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 38.34 - type: f1 value: 37.608755629529455 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 37.839999999999996 - type: f1 value: 36.86898201563507 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 30.936000000000003 - type: f1 value: 30.49401738527071 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 33.75 - type: f1 value: 33.38338946025617 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 13.727 - type: map_at_10 value: 26.740000000000002 - type: map_at_100 value: 28.218 - type: map_at_1000 value: 28.246 - type: map_at_3 value: 21.728 - type: map_at_5 value: 24.371000000000002 - type: ndcg_at_1 value: 13.727 - type: ndcg_at_10 value: 35.07 - type: ndcg_at_100 value: 41.947 - type: ndcg_at_1000 value: 42.649 - type: ndcg_at_3 value: 24.484 - type: ndcg_at_5 value: 29.282999999999998 - type: precision_at_1 value: 13.727 - type: precision_at_10 value: 6.223 - type: precision_at_100 value: 0.9369999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 10.835 - type: precision_at_5 value: 8.848 - type: recall_at_1 value: 13.727 - type: recall_at_10 value: 62.233000000000004 - type: recall_at_100 value: 93.67 - type: recall_at_1000 value: 99.14699999999999 - type: recall_at_3 value: 32.504 - type: recall_at_5 value: 44.239 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 40.553923271901695 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 32.49323183712211 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 55.89811361443445 - type: mrr value: 70.16235764850724 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 82.50506557805856 - type: cos_sim_spearman value: 79.50000423261176 - type: euclidean_pearson value: 75.76190885392926 - type: euclidean_spearman value: 76.7330737163434 - type: manhattan_pearson value: 75.825318036112 - type: manhattan_spearman value: 76.7415076434559 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 75.49060542797494 - type: f1 value: 75.15379262352123 - type: precision value: 74.99391092553932 - type: recall value: 75.49060542797494 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.4182258419546555 - type: f1 value: 0.4182258419546555 - type: precision value: 0.4182258419546555 - type: recall value: 0.4182258419546555 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.013855213023900243 - type: f1 value: 0.0115460108532502 - type: precision value: 0.010391409767925183 - type: recall value: 0.013855213023900243 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.315955766192733 - type: f1 value: 0.315955766192733 - type: precision value: 0.315955766192733 - type: recall value: 0.315955766192733 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 81.74025974025973 - type: f1 value: 81.66568824876 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 33.59451202614059 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 29.128241446157165 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 26.715 - type: map_at_10 value: 35.007 - type: map_at_100 value: 36.352000000000004 - type: map_at_1000 value: 36.51 - type: map_at_3 value: 32.257999999999996 - type: map_at_5 value: 33.595000000000006 - type: ndcg_at_1 value: 33.906 - type: ndcg_at_10 value: 40.353 - type: ndcg_at_100 value: 45.562999999999995 - type: ndcg_at_1000 value: 48.454 - type: ndcg_at_3 value: 36.349 - type: ndcg_at_5 value: 37.856 - type: precision_at_1 value: 33.906 - type: precision_at_10 value: 7.854 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 17.549 - type: precision_at_5 value: 12.561 - type: recall_at_1 value: 26.715 - type: recall_at_10 value: 49.508 - type: recall_at_100 value: 71.76599999999999 - type: recall_at_1000 value: 91.118 - type: recall_at_3 value: 37.356 - type: recall_at_5 value: 41.836 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 19.663 - type: map_at_10 value: 27.086 - type: map_at_100 value: 28.066999999999997 - type: map_at_1000 value: 28.18 - type: map_at_3 value: 24.819 - type: map_at_5 value: 26.332 - type: ndcg_at_1 value: 25.732 - type: ndcg_at_10 value: 31.613999999999997 - type: ndcg_at_100 value: 35.757 - type: ndcg_at_1000 value: 38.21 - type: ndcg_at_3 value: 28.332 - type: ndcg_at_5 value: 30.264000000000003 - type: precision_at_1 value: 25.732 - type: precision_at_10 value: 6.038 - type: precision_at_100 value: 1.034 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 13.864 - type: precision_at_5 value: 10.241999999999999 - type: recall_at_1 value: 19.663 - type: recall_at_10 value: 39.585 - type: recall_at_100 value: 57.718 - type: recall_at_1000 value: 74.26700000000001 - type: recall_at_3 value: 29.845 - type: recall_at_5 value: 35.105 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.125 - type: map_at_10 value: 39.824 - type: map_at_100 value: 40.935 - type: map_at_1000 value: 41.019 - type: map_at_3 value: 37.144 - type: map_at_5 value: 38.647999999999996 - type: ndcg_at_1 value: 34.922 - type: ndcg_at_10 value: 45.072 - type: ndcg_at_100 value: 50.046 - type: ndcg_at_1000 value: 51.895 - type: ndcg_at_3 value: 40.251 - type: ndcg_at_5 value: 42.581 - type: precision_at_1 value: 34.922 - type: precision_at_10 value: 7.303999999999999 - type: precision_at_100 value: 1.0739999999999998 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 17.994 - type: precision_at_5 value: 12.475999999999999 - type: recall_at_1 value: 30.125 - type: recall_at_10 value: 57.253 - type: recall_at_100 value: 79.35799999999999 - type: recall_at_1000 value: 92.523 - type: recall_at_3 value: 44.088 - type: recall_at_5 value: 49.893 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.298000000000002 - type: map_at_10 value: 21.479 - type: map_at_100 value: 22.387 - type: map_at_1000 value: 22.483 - type: map_at_3 value: 19.743 - type: map_at_5 value: 20.444000000000003 - type: ndcg_at_1 value: 17.740000000000002 - type: ndcg_at_10 value: 24.887 - type: ndcg_at_100 value: 29.544999999999998 - type: ndcg_at_1000 value: 32.417 - type: ndcg_at_3 value: 21.274 - type: ndcg_at_5 value: 22.399 - type: precision_at_1 value: 17.740000000000002 - type: precision_at_10 value: 3.932 - type: precision_at_100 value: 0.666 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 8.927 - type: precision_at_5 value: 6.056 - type: recall_at_1 value: 16.298000000000002 - type: recall_at_10 value: 34.031 - type: recall_at_100 value: 55.769000000000005 - type: recall_at_1000 value: 78.19500000000001 - type: recall_at_3 value: 23.799999999999997 - type: recall_at_5 value: 26.562 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 10.958 - type: map_at_10 value: 16.999 - type: map_at_100 value: 17.979 - type: map_at_1000 value: 18.112000000000002 - type: map_at_3 value: 15.010000000000002 - type: map_at_5 value: 16.256999999999998 - type: ndcg_at_1 value: 14.179 - type: ndcg_at_10 value: 20.985 - type: ndcg_at_100 value: 26.216 - type: ndcg_at_1000 value: 29.675 - type: ndcg_at_3 value: 17.28 - type: ndcg_at_5 value: 19.301 - type: precision_at_1 value: 14.179 - type: precision_at_10 value: 3.968 - type: precision_at_100 value: 0.784 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 8.541 - type: precision_at_5 value: 6.468 - type: recall_at_1 value: 10.958 - type: recall_at_10 value: 29.903000000000002 - type: recall_at_100 value: 53.413 - type: recall_at_1000 value: 78.74799999999999 - type: recall_at_3 value: 19.717000000000002 - type: recall_at_5 value: 24.817 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 21.217 - type: map_at_10 value: 29.677 - type: map_at_100 value: 30.928 - type: map_at_1000 value: 31.063000000000002 - type: map_at_3 value: 26.611 - type: map_at_5 value: 28.463 - type: ndcg_at_1 value: 26.083000000000002 - type: ndcg_at_10 value: 35.217 - type: ndcg_at_100 value: 40.715 - type: ndcg_at_1000 value: 43.559 - type: ndcg_at_3 value: 30.080000000000002 - type: ndcg_at_5 value: 32.701 - type: precision_at_1 value: 26.083000000000002 - type: precision_at_10 value: 6.622 - type: precision_at_100 value: 1.115 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 14.629 - type: precision_at_5 value: 10.837 - type: recall_at_1 value: 21.217 - type: recall_at_10 value: 47.031 - type: recall_at_100 value: 70.378 - type: recall_at_1000 value: 89.704 - type: recall_at_3 value: 32.427 - type: recall_at_5 value: 39.31 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 19.274 - type: map_at_10 value: 26.398 - type: map_at_100 value: 27.711000000000002 - type: map_at_1000 value: 27.833000000000002 - type: map_at_3 value: 24.294 - type: map_at_5 value: 25.385 - type: ndcg_at_1 value: 24.886 - type: ndcg_at_10 value: 30.909 - type: ndcg_at_100 value: 36.941 - type: ndcg_at_1000 value: 39.838 - type: ndcg_at_3 value: 27.455000000000002 - type: ndcg_at_5 value: 28.828 - type: precision_at_1 value: 24.886 - type: precision_at_10 value: 5.6739999999999995 - type: precision_at_100 value: 1.0290000000000001 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 13.242 - type: precision_at_5 value: 9.292 - type: recall_at_1 value: 19.274 - type: recall_at_10 value: 39.643 - type: recall_at_100 value: 66.091 - type: recall_at_1000 value: 86.547 - type: recall_at_3 value: 29.602 - type: recall_at_5 value: 33.561 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.653666666666666 - type: map_at_10 value: 25.606666666666666 - type: map_at_100 value: 26.669333333333334 - type: map_at_1000 value: 26.795833333333334 - type: map_at_3 value: 23.43433333333333 - type: map_at_5 value: 24.609666666666666 - type: ndcg_at_1 value: 22.742083333333333 - type: ndcg_at_10 value: 29.978333333333335 - type: ndcg_at_100 value: 34.89808333333333 - type: ndcg_at_1000 value: 37.806583333333336 - type: ndcg_at_3 value: 26.223666666666674 - type: ndcg_at_5 value: 27.91033333333333 - type: precision_at_1 value: 22.742083333333333 - type: precision_at_10 value: 5.397083333333334 - type: precision_at_100 value: 0.9340000000000002 - type: precision_at_1000 value: 0.13691666666666663 - type: precision_at_3 value: 12.331083333333332 - type: precision_at_5 value: 8.805499999999999 - type: recall_at_1 value: 18.653666666666666 - type: recall_at_10 value: 39.22625000000001 - type: recall_at_100 value: 61.31049999999999 - type: recall_at_1000 value: 82.19058333333334 - type: recall_at_3 value: 28.517333333333333 - type: recall_at_5 value: 32.9565 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.07 - type: map_at_10 value: 21.509 - type: map_at_100 value: 22.335 - type: map_at_1000 value: 22.437 - type: map_at_3 value: 19.717000000000002 - type: map_at_5 value: 20.574 - type: ndcg_at_1 value: 18.865000000000002 - type: ndcg_at_10 value: 25.135999999999996 - type: ndcg_at_100 value: 29.483999999999998 - type: ndcg_at_1000 value: 32.303 - type: ndcg_at_3 value: 21.719 - type: ndcg_at_5 value: 23.039 - type: precision_at_1 value: 18.865000000000002 - type: precision_at_10 value: 4.263999999999999 - type: precision_at_100 value: 0.696 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 9.866999999999999 - type: precision_at_5 value: 6.902 - type: recall_at_1 value: 16.07 - type: recall_at_10 value: 33.661 - type: recall_at_100 value: 54.001999999999995 - type: recall_at_1000 value: 75.564 - type: recall_at_3 value: 23.956 - type: recall_at_5 value: 27.264 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 10.847 - type: map_at_10 value: 15.518 - type: map_at_100 value: 16.384 - type: map_at_1000 value: 16.506 - type: map_at_3 value: 14.093 - type: map_at_5 value: 14.868 - type: ndcg_at_1 value: 13.764999999999999 - type: ndcg_at_10 value: 18.766 - type: ndcg_at_100 value: 23.076 - type: ndcg_at_1000 value: 26.344 - type: ndcg_at_3 value: 16.150000000000002 - type: ndcg_at_5 value: 17.373 - type: precision_at_1 value: 13.764999999999999 - type: precision_at_10 value: 3.572 - type: precision_at_100 value: 0.6779999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 7.88 - type: precision_at_5 value: 5.712 - type: recall_at_1 value: 10.847 - type: recall_at_10 value: 25.141999999999996 - type: recall_at_100 value: 44.847 - type: recall_at_1000 value: 68.92099999999999 - type: recall_at_3 value: 17.721999999999998 - type: recall_at_5 value: 20.968999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.377 - type: map_at_10 value: 26.005 - type: map_at_100 value: 26.996 - type: map_at_1000 value: 27.116 - type: map_at_3 value: 23.712 - type: map_at_5 value: 24.859 - type: ndcg_at_1 value: 22.201 - type: ndcg_at_10 value: 30.635 - type: ndcg_at_100 value: 35.623 - type: ndcg_at_1000 value: 38.551 - type: ndcg_at_3 value: 26.565 - type: ndcg_at_5 value: 28.28 - type: precision_at_1 value: 22.201 - type: precision_at_10 value: 5.41 - type: precision_at_100 value: 0.88 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 12.531 - type: precision_at_5 value: 8.806 - type: recall_at_1 value: 18.377 - type: recall_at_10 value: 40.908 - type: recall_at_100 value: 63.563 - type: recall_at_1000 value: 84.503 - type: recall_at_3 value: 29.793999999999997 - type: recall_at_5 value: 34.144999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 20.246 - type: map_at_10 value: 27.528000000000002 - type: map_at_100 value: 28.78 - type: map_at_1000 value: 29.002 - type: map_at_3 value: 25.226 - type: map_at_5 value: 26.355 - type: ndcg_at_1 value: 25.099 - type: ndcg_at_10 value: 32.421 - type: ndcg_at_100 value: 37.2 - type: ndcg_at_1000 value: 40.693 - type: ndcg_at_3 value: 28.768 - type: ndcg_at_5 value: 30.23 - type: precision_at_1 value: 25.099 - type: precision_at_10 value: 6.245 - type: precision_at_100 value: 1.269 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 13.767999999999999 - type: precision_at_5 value: 9.881 - type: recall_at_1 value: 20.246 - type: recall_at_10 value: 41.336 - type: recall_at_100 value: 63.098 - type: recall_at_1000 value: 86.473 - type: recall_at_3 value: 30.069000000000003 - type: recall_at_5 value: 34.262 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 14.054 - type: map_at_10 value: 20.25 - type: map_at_100 value: 21.178 - type: map_at_1000 value: 21.288999999999998 - type: map_at_3 value: 18.584999999999997 - type: map_at_5 value: 19.536 - type: ndcg_at_1 value: 15.527 - type: ndcg_at_10 value: 23.745 - type: ndcg_at_100 value: 28.610999999999997 - type: ndcg_at_1000 value: 31.740000000000002 - type: ndcg_at_3 value: 20.461 - type: ndcg_at_5 value: 22.072 - type: precision_at_1 value: 15.527 - type: precision_at_10 value: 3.882 - type: precision_at_100 value: 0.6930000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 9.181000000000001 - type: precision_at_5 value: 6.433 - type: recall_at_1 value: 14.054 - type: recall_at_10 value: 32.714 - type: recall_at_100 value: 55.723 - type: recall_at_1000 value: 79.72399999999999 - type: recall_at_3 value: 23.832 - type: recall_at_5 value: 27.754 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 6.122 - type: map_at_10 value: 11.556 - type: map_at_100 value: 12.998000000000001 - type: map_at_1000 value: 13.202 - type: map_at_3 value: 9.657 - type: map_at_5 value: 10.585 - type: ndcg_at_1 value: 15.049000000000001 - type: ndcg_at_10 value: 17.574 - type: ndcg_at_100 value: 24.465999999999998 - type: ndcg_at_1000 value: 28.511999999999997 - type: ndcg_at_3 value: 13.931 - type: ndcg_at_5 value: 15.112 - type: precision_at_1 value: 15.049000000000001 - type: precision_at_10 value: 5.831 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 10.749 - type: precision_at_5 value: 8.365 - type: recall_at_1 value: 6.122 - type: recall_at_10 value: 22.207 - type: recall_at_100 value: 47.08 - type: recall_at_1000 value: 70.182 - type: recall_at_3 value: 13.416 - type: recall_at_5 value: 16.672 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: f097057d03ed98220bc7309ddb10b71a54d667d6 metrics: - type: map_at_1 value: 4.672 - type: map_at_10 value: 10.534 - type: map_at_100 value: 14.798 - type: map_at_1000 value: 15.927 - type: map_at_3 value: 7.317 - type: map_at_5 value: 8.726 - type: ndcg_at_1 value: 36.5 - type: ndcg_at_10 value: 26.098 - type: ndcg_at_100 value: 29.215999999999998 - type: ndcg_at_1000 value: 36.254999999999995 - type: ndcg_at_3 value: 29.247 - type: ndcg_at_5 value: 27.692 - type: precision_at_1 value: 47.25 - type: precision_at_10 value: 22.625 - type: precision_at_100 value: 7.042 - type: precision_at_1000 value: 1.6129999999999998 - type: precision_at_3 value: 34.083000000000006 - type: precision_at_5 value: 29.5 - type: recall_at_1 value: 4.672 - type: recall_at_10 value: 15.638 - type: recall_at_100 value: 36.228 - type: recall_at_1000 value: 58.831 - type: recall_at_3 value: 8.578 - type: recall_at_5 value: 11.18 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 829147f8f75a25f005913200eb5ed41fae320aa1 metrics: - type: accuracy value: 49.919999999999995 - type: f1 value: 45.37973678791632 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: 1429cf27e393599b8b359b9b72c666f96b2525f9 metrics: - type: map_at_1 value: 25.801000000000002 - type: map_at_10 value: 33.941 - type: map_at_100 value: 34.73 - type: map_at_1000 value: 34.793 - type: map_at_3 value: 31.705 - type: map_at_5 value: 33.047 - type: ndcg_at_1 value: 27.933000000000003 - type: ndcg_at_10 value: 38.644 - type: ndcg_at_100 value: 42.594 - type: ndcg_at_1000 value: 44.352000000000004 - type: ndcg_at_3 value: 34.199 - type: ndcg_at_5 value: 36.573 - type: precision_at_1 value: 27.933000000000003 - type: precision_at_10 value: 5.603000000000001 - type: precision_at_100 value: 0.773 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 14.171 - type: precision_at_5 value: 9.786999999999999 - type: recall_at_1 value: 25.801000000000002 - type: recall_at_10 value: 50.876 - type: recall_at_100 value: 69.253 - type: recall_at_1000 value: 82.907 - type: recall_at_3 value: 38.879000000000005 - type: recall_at_5 value: 44.651999999999994 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be metrics: - type: map_at_1 value: 9.142 - type: map_at_10 value: 13.841999999999999 - type: map_at_100 value: 14.960999999999999 - type: map_at_1000 value: 15.187000000000001 - type: map_at_3 value: 11.966000000000001 - type: map_at_5 value: 12.921 - type: ndcg_at_1 value: 18.364 - type: ndcg_at_10 value: 18.590999999999998 - type: ndcg_at_100 value: 24.153 - type: ndcg_at_1000 value: 29.104000000000003 - type: ndcg_at_3 value: 16.323 - type: ndcg_at_5 value: 17.000999999999998 - type: precision_at_1 value: 18.364 - type: precision_at_10 value: 5.216 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 10.751 - type: precision_at_5 value: 7.932 - type: recall_at_1 value: 9.142 - type: recall_at_10 value: 22.747 - type: recall_at_100 value: 44.585 - type: recall_at_1000 value: 75.481 - type: recall_at_3 value: 14.602 - type: recall_at_5 value: 17.957 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: 766870b35a1b9ca65e67a0d1913899973551fc6c metrics: - type: map_at_1 value: 18.677 - type: map_at_10 value: 26.616 - type: map_at_100 value: 27.605 - type: map_at_1000 value: 27.711999999999996 - type: map_at_3 value: 24.396 - type: map_at_5 value: 25.627 - type: ndcg_at_1 value: 37.352999999999994 - type: ndcg_at_10 value: 33.995 - type: ndcg_at_100 value: 38.423 - type: ndcg_at_1000 value: 40.947 - type: ndcg_at_3 value: 29.885 - type: ndcg_at_5 value: 31.874999999999996 - type: precision_at_1 value: 37.352999999999994 - type: precision_at_10 value: 7.539999999999999 - type: precision_at_100 value: 1.107 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.938 - type: precision_at_5 value: 12.943 - type: recall_at_1 value: 18.677 - type: recall_at_10 value: 37.698 - type: recall_at_100 value: 55.354000000000006 - type: recall_at_1000 value: 72.255 - type: recall_at_3 value: 28.406 - type: recall_at_5 value: 32.357 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4 metrics: - type: accuracy value: 74.3292 - type: ap value: 68.30186110189658 - type: f1 value: 74.20709636944783 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: validation revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849 metrics: - type: map_at_1 value: 6.889000000000001 - type: map_at_10 value: 12.321 - type: map_at_100 value: 13.416 - type: map_at_1000 value: 13.525 - type: map_at_3 value: 10.205 - type: map_at_5 value: 11.342 - type: ndcg_at_1 value: 7.092 - type: ndcg_at_10 value: 15.827 - type: ndcg_at_100 value: 21.72 - type: ndcg_at_1000 value: 24.836 - type: ndcg_at_3 value: 11.393 - type: ndcg_at_5 value: 13.462 - type: precision_at_1 value: 7.092 - type: precision_at_10 value: 2.7969999999999997 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 5.019 - type: precision_at_5 value: 4.06 - type: recall_at_1 value: 6.889000000000001 - type: recall_at_10 value: 26.791999999999998 - type: recall_at_100 value: 55.371 - type: recall_at_1000 value: 80.12899999999999 - type: recall_at_3 value: 14.573 - type: recall_at_5 value: 19.557 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 89.6374829001368 - type: f1 value: 89.20878379358307 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 84.54212454212454 - type: f1 value: 82.81080100037023 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 86.46430953969313 - type: f1 value: 86.00019824223267 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 81.31850923896022 - type: f1 value: 81.07860454762863 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 58.23234134098243 - type: f1 value: 56.63845098081841 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 72.28571428571429 - type: f1 value: 70.95796714592039 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 70.68171454628363 - type: f1 value: 52.57188062729139 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 60.521273598196665 - type: f1 value: 42.70492970339204 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 64.32288192128087 - type: f1 value: 45.97360620220273 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 58.67209520826808 - type: f1 value: 42.82844991304579 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) config: hi split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 41.95769092864826 - type: f1 value: 28.914127631431263 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) config: th split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 55.28390596745027 - type: f1 value: 38.33899250561289 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea metrics: - type: accuracy value: 70.00336247478144 - type: f1 value: 68.72041942191649 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.0268997982515 - type: f1 value: 75.29844481506652 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: dcefc037ef84348e49b0d29109e891c01067226b metrics: - type: v_measure value: 30.327566856300813 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc metrics: - type: v_measure value: 28.01650210863619 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.11041256752524 - type: mrr value: 32.14172939750204 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610 metrics: - type: map_at_1 value: 3.527 - type: map_at_10 value: 9.283 - type: map_at_100 value: 11.995000000000001 - type: map_at_1000 value: 13.33 - type: map_at_3 value: 6.223 - type: map_at_5 value: 7.68 - type: ndcg_at_1 value: 36.223 - type: ndcg_at_10 value: 28.255999999999997 - type: ndcg_at_100 value: 26.355 - type: ndcg_at_1000 value: 35.536 - type: ndcg_at_3 value: 31.962000000000003 - type: ndcg_at_5 value: 30.61 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 21.889 - type: precision_at_100 value: 7.1080000000000005 - type: precision_at_1000 value: 1.989 - type: precision_at_3 value: 30.857 - type: precision_at_5 value: 27.307 - type: recall_at_1 value: 3.527 - type: recall_at_10 value: 14.015 - type: recall_at_100 value: 28.402 - type: recall_at_1000 value: 59.795 - type: recall_at_3 value: 7.5969999999999995 - type: recall_at_5 value: 10.641 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c metrics: - type: map_at_1 value: 11.631 - type: map_at_10 value: 19.532 - type: map_at_100 value: 20.821 - type: map_at_1000 value: 20.910999999999998 - type: map_at_3 value: 16.597 - type: map_at_5 value: 18.197 - type: ndcg_at_1 value: 13.413 - type: ndcg_at_10 value: 24.628 - type: ndcg_at_100 value: 30.883 - type: ndcg_at_1000 value: 33.216 - type: ndcg_at_3 value: 18.697 - type: ndcg_at_5 value: 21.501 - type: precision_at_1 value: 13.413 - type: precision_at_10 value: 4.571 - type: precision_at_100 value: 0.812 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.845 - type: precision_at_5 value: 6.889000000000001 - type: recall_at_1 value: 11.631 - type: recall_at_10 value: 38.429 - type: recall_at_100 value: 67.009 - type: recall_at_1000 value: 84.796 - type: recall_at_3 value: 22.74 - type: recall_at_5 value: 29.266 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: 6205996560df11e3a3da9ab4f926788fc30a7db4 metrics: - type: map_at_1 value: 66.64 - type: map_at_10 value: 80.394 - type: map_at_100 value: 81.099 - type: map_at_1000 value: 81.122 - type: map_at_3 value: 77.289 - type: map_at_5 value: 79.25999999999999 - type: ndcg_at_1 value: 76.85 - type: ndcg_at_10 value: 84.68 - type: ndcg_at_100 value: 86.311 - type: ndcg_at_1000 value: 86.49900000000001 - type: ndcg_at_3 value: 81.295 - type: ndcg_at_5 value: 83.199 - type: precision_at_1 value: 76.85 - type: precision_at_10 value: 12.928999999999998 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.557 - type: precision_at_5 value: 23.576 - type: recall_at_1 value: 66.64 - type: recall_at_10 value: 93.059 - type: recall_at_100 value: 98.922 - type: recall_at_1000 value: 99.883 - type: recall_at_3 value: 83.49499999999999 - type: recall_at_5 value: 88.729 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: b2805658ae38990172679479369a78b86de8c390 metrics: - type: v_measure value: 42.17131361041068 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 48.01815621479994 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5 metrics: - type: map_at_1 value: 3.198 - type: map_at_10 value: 7.550999999999999 - type: map_at_100 value: 9.232 - type: map_at_1000 value: 9.51 - type: map_at_3 value: 5.2940000000000005 - type: map_at_5 value: 6.343999999999999 - type: ndcg_at_1 value: 15.8 - type: ndcg_at_10 value: 13.553999999999998 - type: ndcg_at_100 value: 20.776 - type: ndcg_at_1000 value: 26.204 - type: ndcg_at_3 value: 12.306000000000001 - type: ndcg_at_5 value: 10.952 - type: precision_at_1 value: 15.8 - type: precision_at_10 value: 7.180000000000001 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.307 - type: precision_at_3 value: 11.333 - type: precision_at_5 value: 9.62 - type: recall_at_1 value: 3.198 - type: recall_at_10 value: 14.575 - type: recall_at_100 value: 35.758 - type: recall_at_1000 value: 62.317 - type: recall_at_3 value: 6.922000000000001 - type: recall_at_5 value: 9.767000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cos_sim_pearson value: 84.5217161312271 - type: cos_sim_spearman value: 79.58562467776268 - type: euclidean_pearson value: 76.69364353942403 - type: euclidean_spearman value: 74.68959282070473 - type: manhattan_pearson value: 76.81159265133732 - type: manhattan_spearman value: 74.7519444048176 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f metrics: - type: cos_sim_pearson value: 83.70403706922605 - type: cos_sim_spearman value: 74.28502198729447 - type: euclidean_pearson value: 83.32719404608066 - type: euclidean_spearman value: 75.92189433460788 - type: manhattan_pearson value: 83.35841543005293 - type: manhattan_spearman value: 75.94458615451978 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9 metrics: - type: cos_sim_pearson value: 84.94127878986795 - type: cos_sim_spearman value: 85.35148434923192 - type: euclidean_pearson value: 81.71127467071571 - type: euclidean_spearman value: 82.88240481546771 - type: manhattan_pearson value: 81.72826221967252 - type: manhattan_spearman value: 82.90725064625128 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b metrics: - type: cos_sim_pearson value: 83.1474704168523 - type: cos_sim_spearman value: 79.20612995350827 - type: euclidean_pearson value: 78.85993329596555 - type: euclidean_spearman value: 78.91956572744715 - type: manhattan_pearson value: 78.89999720522347 - type: manhattan_spearman value: 78.93956842550107 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6 metrics: - type: cos_sim_pearson value: 84.81255514055894 - type: cos_sim_spearman value: 85.5217140762934 - type: euclidean_pearson value: 82.15024353784499 - type: euclidean_spearman value: 83.04155334389833 - type: manhattan_pearson value: 82.18598945053624 - type: manhattan_spearman value: 83.07248357693301 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd metrics: - type: cos_sim_pearson value: 80.63248465157822 - type: cos_sim_spearman value: 82.53853238521991 - type: euclidean_pearson value: 78.33936863828221 - type: euclidean_spearman value: 79.16305579487414 - type: manhattan_pearson value: 78.3888359870894 - type: manhattan_spearman value: 79.18504473136467 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0 metrics: - type: cos_sim_pearson value: 90.09066290639687 - type: cos_sim_spearman value: 90.43893699357069 - type: euclidean_pearson value: 82.39520777222396 - type: euclidean_spearman value: 81.23948185395952 - type: manhattan_pearson value: 82.35529784653383 - type: manhattan_spearman value: 81.12681522483975 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906 metrics: - type: cos_sim_pearson value: 63.52752323046846 - type: cos_sim_spearman value: 63.19719780439462 - type: euclidean_pearson value: 58.29085490641428 - type: euclidean_spearman value: 58.975178656335046 - type: manhattan_pearson value: 58.183542772416985 - type: manhattan_spearman value: 59.190630462178994 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: 8913289635987208e6e7c72789e4be2fe94b6abd metrics: - type: cos_sim_pearson value: 85.45100366635687 - type: cos_sim_spearman value: 85.66816193002651 - type: euclidean_pearson value: 81.87976731329091 - type: euclidean_spearman value: 82.01382867690964 - type: manhattan_pearson value: 81.88260155706726 - type: manhattan_spearman value: 82.05258597906492 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: 56a6d0140cf6356659e2a7c1413286a774468d44 metrics: - type: map value: 77.53549990038017 - type: mrr value: 93.37474163454556 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: a75ae049398addde9b70f6b268875f5cbce99089 metrics: - type: map_at_1 value: 31.167 - type: map_at_10 value: 40.778 - type: map_at_100 value: 42.063 - type: map_at_1000 value: 42.103 - type: map_at_3 value: 37.12 - type: map_at_5 value: 39.205 - type: ndcg_at_1 value: 33.667 - type: ndcg_at_10 value: 46.662 - type: ndcg_at_100 value: 51.995999999999995 - type: ndcg_at_1000 value: 53.254999999999995 - type: ndcg_at_3 value: 39.397999999999996 - type: ndcg_at_5 value: 42.934 - type: precision_at_1 value: 33.667 - type: precision_at_10 value: 7.1 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 16.111 - type: precision_at_5 value: 11.600000000000001 - type: recall_at_1 value: 31.167 - type: recall_at_10 value: 63.744 - type: recall_at_100 value: 87.156 - type: recall_at_1000 value: 97.556 - type: recall_at_3 value: 44.0 - type: recall_at_5 value: 52.556000000000004 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea metrics: - type: cos_sim_accuracy value: 99.55148514851486 - type: cos_sim_ap value: 80.535236573428 - type: cos_sim_f1 value: 75.01331912626532 - type: cos_sim_precision value: 80.27366020524515 - type: cos_sim_recall value: 70.39999999999999 - type: dot_accuracy value: 99.04851485148515 - type: dot_ap value: 28.505358821499726 - type: dot_f1 value: 36.36363636363637 - type: dot_precision value: 37.160751565762006 - type: dot_recall value: 35.6 - type: euclidean_accuracy value: 99.4990099009901 - type: euclidean_ap value: 74.95819047075476 - type: euclidean_f1 value: 71.15489874110564 - type: euclidean_precision value: 78.59733978234583 - type: euclidean_recall value: 65.0 - type: manhattan_accuracy value: 99.50198019801981 - type: manhattan_ap value: 75.02070096015086 - type: manhattan_f1 value: 71.20535714285712 - type: manhattan_precision value: 80.55555555555556 - type: manhattan_recall value: 63.800000000000004 - type: max_accuracy value: 99.55148514851486 - type: max_ap value: 80.535236573428 - type: max_f1 value: 75.01331912626532 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235 metrics: - type: v_measure value: 54.13314692311623 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0 metrics: - type: v_measure value: 31.115181648287145 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9 metrics: - type: map value: 44.771112666694336 - type: mrr value: 45.30415764790765 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122 metrics: - type: cos_sim_pearson value: 30.849429597669374 - type: cos_sim_spearman value: 30.384175038360194 - type: dot_pearson value: 29.030383429536823 - type: dot_spearman value: 28.03273624951732 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217 metrics: - type: map_at_1 value: 0.19499999999999998 - type: map_at_10 value: 1.0959999999999999 - type: map_at_100 value: 5.726 - type: map_at_1000 value: 13.611999999999998 - type: map_at_3 value: 0.45399999999999996 - type: map_at_5 value: 0.67 - type: ndcg_at_1 value: 71.0 - type: ndcg_at_10 value: 55.352999999999994 - type: ndcg_at_100 value: 40.797 - type: ndcg_at_1000 value: 35.955999999999996 - type: ndcg_at_3 value: 63.263000000000005 - type: ndcg_at_5 value: 60.14000000000001 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 56.99999999999999 - type: precision_at_100 value: 41.199999999999996 - type: precision_at_1000 value: 16.154 - type: precision_at_3 value: 66.667 - type: precision_at_5 value: 62.8 - type: recall_at_1 value: 0.19499999999999998 - type: recall_at_10 value: 1.3639999999999999 - type: recall_at_100 value: 9.317 - type: recall_at_1000 value: 33.629999999999995 - type: recall_at_3 value: 0.49300000000000005 - type: recall_at_5 value: 0.756 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b metrics: - type: map_at_1 value: 1.335 - type: map_at_10 value: 6.293 - type: map_at_100 value: 10.928 - type: map_at_1000 value: 12.359 - type: map_at_3 value: 3.472 - type: map_at_5 value: 4.935 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 16.178 - type: ndcg_at_100 value: 28.149 - type: ndcg_at_1000 value: 39.845000000000006 - type: ndcg_at_3 value: 19.171 - type: ndcg_at_5 value: 17.864 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 14.49 - type: precision_at_100 value: 6.306000000000001 - type: precision_at_1000 value: 1.3860000000000001 - type: precision_at_3 value: 21.088 - type: precision_at_5 value: 18.367 - type: recall_at_1 value: 1.335 - type: recall_at_10 value: 10.825999999999999 - type: recall_at_100 value: 39.251000000000005 - type: recall_at_1000 value: 74.952 - type: recall_at_3 value: 4.9110000000000005 - type: recall_at_5 value: 7.312 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 69.93339999999999 - type: ap value: 13.87476602492533 - type: f1 value: 53.867357615848555 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: 62146448f05be9e52a36b8ee9936447ea787eede metrics: - type: accuracy value: 62.43916242218449 - type: f1 value: 62.870386304954685 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4 metrics: - type: v_measure value: 37.202082549859796 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.65023544137807 - type: cos_sim_ap value: 65.99787692764193 - type: cos_sim_f1 value: 62.10650887573965 - type: cos_sim_precision value: 56.30901287553648 - type: cos_sim_recall value: 69.23482849604221 - type: dot_accuracy value: 79.10830303391549 - type: dot_ap value: 48.80109642320246 - type: dot_f1 value: 51.418744625967314 - type: dot_precision value: 40.30253107683091 - type: dot_recall value: 71.00263852242745 - type: euclidean_accuracy value: 82.45812719794957 - type: euclidean_ap value: 60.09969493259607 - type: euclidean_f1 value: 57.658573789246226 - type: euclidean_precision value: 55.62913907284768 - type: euclidean_recall value: 59.84168865435356 - type: manhattan_accuracy value: 82.46408773916671 - type: manhattan_ap value: 60.116199786815116 - type: manhattan_f1 value: 57.683903860160235 - type: manhattan_precision value: 53.41726618705036 - type: manhattan_recall value: 62.69129287598945 - type: max_accuracy value: 83.65023544137807 - type: max_ap value: 65.99787692764193 - type: max_f1 value: 62.10650887573965 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.34943920518494 - type: cos_sim_ap value: 84.5428891020442 - type: cos_sim_f1 value: 77.09709933923172 - type: cos_sim_precision value: 74.83150952967607 - type: cos_sim_recall value: 79.50415768401602 - type: dot_accuracy value: 84.53448208949432 - type: dot_ap value: 73.96328242371995 - type: dot_f1 value: 70.00553786515299 - type: dot_precision value: 63.58777665995976 - type: dot_recall value: 77.86418232214352 - type: euclidean_accuracy value: 86.87662514068381 - type: euclidean_ap value: 81.45499631520235 - type: euclidean_f1 value: 73.46567109816063 - type: euclidean_precision value: 69.71037533697381 - type: euclidean_recall value: 77.6485987064983 - type: manhattan_accuracy value: 86.88244654014825 - type: manhattan_ap value: 81.47180273946366 - type: manhattan_f1 value: 73.44624393136418 - type: manhattan_precision value: 70.80385852090032 - type: manhattan_recall value: 76.29350169387126 - type: max_accuracy value: 88.34943920518494 - type: max_ap value: 84.5428891020442 - type: max_f1 value: 77.09709933923172 --- # SGPT-5.8B-weightedmean-msmarco-specb-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 249592 with parameters: ``` {'batch_size': 2, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTJModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
lewtun/my-awesome-setfit-model-3	lewtun	2022-10-03T09:06:25Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-03T09:06:17Z	--- 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 768 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, mean 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 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, '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 -->
tkubotake/distilbert-base-uncased-finetuned-emotion	tkubotake	2022-10-03T08:25:34Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-28T05:01:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.918 - name: F1 type: f1 value: 0.9179456491632857 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.918 - F1: 0.9179 ## 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: 64 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8566 \| 1.0 \| 250 \| 0.3283 \| 0.903 \| 0.9002 \| \| 0.2607 \| 2.0 \| 500 \| 0.2263 \| 0.918 \| 0.9179 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
PartiallyTyped/answerable_tydiqa_lm_pretrained_finnish	PartiallyTyped	2022-10-03T07:09:57Z	119	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "text generation", "fi", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-02T14:52:13Z	--- language: - fi tags: - text generation license: mit datasets: - answerable tydiqa --- # ReadMe This is a pretrained model based on [Finnish-NLP/gpt2-finnish](https://huggingface.co/Finnish-NLP/gpt2-finnish) that has been trained on [copenlu/answerable_tydiqa](https://huggingface.co/datasets/copenlu/answerable_tydiqa), specifically the text field of the Finnish samples for 2 epochs. To use the pretrained head, use: `AutoModelForCausalLM.from_pretrained`. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PartiallyTyped/answerable_tydiqa_lm_pretrained_finnish" model = AutoModelForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) ```
krm/my_exercice_mrpc	krm	2022-10-03T06:47:03Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-03T06:15:31Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: krm/my_exercice_mrpc results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # krm/my_exercice_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6942 - Train Accuracy: 0.6200 - Validation Loss: 0.6486 - Validation Accuracy: 0.6838 - Epoch: 2 ## Model description Ce modèle n'est pas à utiliser. Il s'agit d'un petit essai. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.6860 \| 0.6314 \| 0.7727 \| 0.6838 \| 0 \| \| 0.6862 \| 0.6347 \| 0.6326 \| 0.6838 \| 1 \| \| 0.6942 \| 0.6200 \| 0.6486 \| 0.6838 \| 2 \| ### Framework versions - Transformers 4.22.2 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/liminalspaces	sd-concepts-library	2022-10-03T04:23:32Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-10-03T04:23:28Z	--- license: mit --- ### Liminalspaces on Stable Diffusion This is the `<liminal image>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<liminal image> 0](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/1.jpeg) ![<liminal image> 1](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/4.jpeg) ![<liminal image> 2](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/2.jpeg) ![<liminal image> 3](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/0.jpeg) ![<liminal image> 4](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/3.jpeg) ![<liminal image> 5](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/5.jpeg)
g30rv17ys/ddpm-geeve-cnv-1500-300ep	g30rv17ys	2022-10-03T01:51:32Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-02T16:17:04Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-cnv-1500-300ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-1500-300ep/tensorboard?#scalars)
Arman511/CatVsDog	Arman511	2022-10-03T00:02:30Z	0	0	fastai	[ "fastai", "region:us" ]	null	2022-10-03T00:01:08Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
din0s/t5-small-fr-finetuned-en-to-it	din0s	2022-10-02T22:46:16Z	112	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccmatrix", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-02T22:08:17Z	--- tags: - generated_from_trainer datasets: - ccmatrix metrics: - bleu model-index: - name: t5-small_fr-finetuned-en-to-it results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: ccmatrix type: ccmatrix config: en-it split: train[3000:12000] args: en-it metrics: - name: Bleu type: bleu value: 7.4222 --- <!-- 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_fr-finetuned-en-to-it This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-fr](https://huggingface.co/din0s/t5-small-finetuned-en-to-fr) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.3225 - Bleu: 7.4222 - Gen Len: 59.1127 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 94 \| 3.0406 \| 3.2546 \| 52.6127 \| \| No log \| 2.0 \| 188 \| 2.9278 \| 3.1206 \| 62.774 \| \| No log \| 3.0 \| 282 \| 2.8573 \| 3.4206 \| 63.6707 \| \| No log \| 4.0 \| 376 \| 2.8030 \| 3.4847 \| 66.408 \| \| No log \| 5.0 \| 470 \| 2.7602 \| 3.8933 \| 64.362 \| \| 3.2982 \| 6.0 \| 564 \| 2.7185 \| 3.9298 \| 66.058 \| \| 3.2982 \| 7.0 \| 658 \| 2.6842 \| 4.0344 \| 65.5773 \| \| 3.2982 \| 8.0 \| 752 \| 2.6536 \| 4.3243 \| 65.0047 \| \| 3.2982 \| 9.0 \| 846 \| 2.6233 \| 4.5078 \| 64.5813 \| \| 3.2982 \| 10.0 \| 940 \| 2.5966 \| 4.6657 \| 63.654 \| \| 2.9837 \| 11.0 \| 1034 \| 2.5743 \| 4.7664 \| 63.326 \| \| 2.9837 \| 12.0 \| 1128 \| 2.5526 \| 4.9535 \| 62.7327 \| \| 2.9837 \| 13.0 \| 1222 \| 2.5303 \| 5.1386 \| 63.5887 \| \| 2.9837 \| 14.0 \| 1316 \| 2.5122 \| 5.1037 \| 64.1667 \| \| 2.9837 \| 15.0 \| 1410 \| 2.4937 \| 5.3304 \| 63.116 \| \| 2.8416 \| 16.0 \| 1504 \| 2.4797 \| 5.5006 \| 61.4953 \| \| 2.8416 \| 17.0 \| 1598 \| 2.4627 \| 5.5892 \| 62.01 \| \| 2.8416 \| 18.0 \| 1692 \| 2.4497 \| 5.8497 \| 61.42 \| \| 2.8416 \| 19.0 \| 1786 \| 2.4372 \| 6.0074 \| 61.1587 \| \| 2.8416 \| 20.0 \| 1880 \| 2.4256 \| 6.1464 \| 60.522 \| \| 2.8416 \| 21.0 \| 1974 \| 2.4148 \| 6.3117 \| 59.5567 \| \| 2.7428 \| 22.0 \| 2068 \| 2.4039 \| 6.4626 \| 59.532 \| \| 2.7428 \| 23.0 \| 2162 \| 2.3939 \| 6.5287 \| 60.2307 \| \| 2.7428 \| 24.0 \| 2256 \| 2.3857 \| 6.6093 \| 60.22 \| \| 2.7428 \| 25.0 \| 2350 \| 2.3772 \| 6.8004 \| 59.396 \| \| 2.7428 \| 26.0 \| 2444 \| 2.3703 \| 6.9433 \| 59.5027 \| \| 2.6779 \| 27.0 \| 2538 \| 2.3631 \| 7.0153 \| 59.1433 \| \| 2.6779 \| 28.0 \| 2632 \| 2.3575 \| 7.1783 \| 58.9793 \| \| 2.6779 \| 29.0 \| 2726 \| 2.3514 \| 7.1639 \| 59.362 \| \| 2.6779 \| 30.0 \| 2820 \| 2.3457 \| 7.2176 \| 58.9927 \| \| 2.6779 \| 31.0 \| 2914 \| 2.3411 \| 7.2599 \| 59.1433 \| \| 2.6335 \| 32.0 \| 3008 \| 2.3374 \| 7.284 \| 59.1787 \| \| 2.6335 \| 33.0 \| 3102 \| 2.3339 \| 7.3678 \| 59.07 \| \| 2.6335 \| 34.0 \| 3196 \| 2.3307 \| 7.3364 \| 58.9813 \| \| 2.6335 \| 35.0 \| 3290 \| 2.3281 \| 7.3318 \| 58.96 \| \| 2.6335 \| 36.0 \| 3384 \| 2.3259 \| 7.394 \| 59.0787 \| \| 2.6335 \| 37.0 \| 3478 \| 2.3245 \| 7.4133 \| 59.0393 \| \| 2.609 \| 38.0 \| 3572 \| 2.3232 \| 7.383 \| 59.1887 \| \| 2.609 \| 39.0 \| 3666 \| 2.3227 \| 7.4105 \| 59.1227 \| \| 2.609 \| 40.0 \| 3760 \| 2.3225 \| 7.4222 \| 59.1127 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0
din0s/t5-small-de-finetuned-en-to-it	din0s	2022-10-02T22:43:18Z	108	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccmatrix", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-02T22:01:53Z	--- tags: - generated_from_trainer datasets: - ccmatrix metrics: - bleu model-index: - name: t5-small_de-finetuned-en-to-it results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: ccmatrix type: ccmatrix config: en-it split: train[3000:12000] args: en-it metrics: - name: Bleu type: bleu value: 6.7338 --- <!-- 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_de-finetuned-en-to-it This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-de](https://huggingface.co/din0s/t5-small-finetuned-en-to-de) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.3480 - Bleu: 6.7338 - Gen Len: 61.3273 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 94 \| 3.1064 \| 2.9057 \| 47.5067 \| \| No log \| 2.0 \| 188 \| 2.9769 \| 2.7484 \| 76.9273 \| \| No log \| 3.0 \| 282 \| 2.9015 \| 3.0624 \| 79.8873 \| \| No log \| 4.0 \| 376 \| 2.8444 \| 3.2959 \| 78.276 \| \| No log \| 5.0 \| 470 \| 2.7989 \| 3.6694 \| 74.6013 \| \| 3.3505 \| 6.0 \| 564 \| 2.7564 \| 3.8098 \| 74.3247 \| \| 3.3505 \| 7.0 \| 658 \| 2.7212 \| 3.9596 \| 72.554 \| \| 3.3505 \| 8.0 \| 752 \| 2.6886 \| 4.2231 \| 70.7673 \| \| 3.3505 \| 9.0 \| 846 \| 2.6572 \| 4.1466 \| 72.0113 \| \| 3.3505 \| 10.0 \| 940 \| 2.6294 \| 4.2696 \| 71.1647 \| \| 3.0254 \| 11.0 \| 1034 \| 2.6064 \| 4.6375 \| 67.7707 \| \| 3.0254 \| 12.0 \| 1128 \| 2.5838 \| 4.7208 \| 68.6707 \| \| 3.0254 \| 13.0 \| 1222 \| 2.5614 \| 4.9191 \| 68.5767 \| \| 3.0254 \| 14.0 \| 1316 \| 2.5427 \| 4.9837 \| 66.3867 \| \| 3.0254 \| 15.0 \| 1410 \| 2.5241 \| 5.1011 \| 66.7667 \| \| 2.8789 \| 16.0 \| 1504 \| 2.5093 \| 5.283 \| 64.944 \| \| 2.8789 \| 17.0 \| 1598 \| 2.4919 \| 5.3205 \| 65.738 \| \| 2.8789 \| 18.0 \| 1692 \| 2.4788 \| 5.3046 \| 65.3207 \| \| 2.8789 \| 19.0 \| 1786 \| 2.4651 \| 5.5282 \| 64.9407 \| \| 2.8789 \| 20.0 \| 1880 \| 2.4532 \| 5.6745 \| 63.0873 \| \| 2.8789 \| 21.0 \| 1974 \| 2.4419 \| 5.7073 \| 63.4973 \| \| 2.7782 \| 22.0 \| 2068 \| 2.4308 \| 5.8513 \| 62.8813 \| \| 2.7782 \| 23.0 \| 2162 \| 2.4209 \| 5.8267 \| 64.1033 \| \| 2.7782 \| 24.0 \| 2256 \| 2.4124 \| 5.8534 \| 64.2993 \| \| 2.7782 \| 25.0 \| 2350 \| 2.4037 \| 6.0406 \| 63.8313 \| \| 2.7782 \| 26.0 \| 2444 \| 2.3964 \| 6.1517 \| 63.4213 \| \| 2.7116 \| 27.0 \| 2538 \| 2.3897 \| 6.2175 \| 63.0573 \| \| 2.7116 \| 28.0 \| 2632 \| 2.3836 \| 6.2551 \| 62.876 \| \| 2.7116 \| 29.0 \| 2726 \| 2.3777 \| 6.4412 \| 62.4167 \| \| 2.7116 \| 30.0 \| 2820 \| 2.3717 \| 6.4604 \| 62.1087 \| \| 2.7116 \| 31.0 \| 2914 \| 2.3673 \| 6.5471 \| 62.1373 \| \| 2.6662 \| 32.0 \| 3008 \| 2.3634 \| 6.5296 \| 62.2533 \| \| 2.6662 \| 33.0 \| 3102 \| 2.3596 \| 6.6623 \| 61.276 \| \| 2.6662 \| 34.0 \| 3196 \| 2.3564 \| 6.6591 \| 61.392 \| \| 2.6662 \| 35.0 \| 3290 \| 2.3539 \| 6.7201 \| 61.0827 \| \| 2.6662 \| 36.0 \| 3384 \| 2.3516 \| 6.675 \| 61.3173 \| \| 2.6662 \| 37.0 \| 3478 \| 2.3500 \| 6.6894 \| 61.3507 \| \| 2.6411 \| 38.0 \| 3572 \| 2.3488 \| 6.6539 \| 61.5253 \| \| 2.6411 \| 39.0 \| 3666 \| 2.3482 \| 6.7135 \| 61.3733 \| \| 2.6411 \| 40.0 \| 3760 \| 2.3480 \| 6.7338 \| 61.3273 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0
ChrisC1657/Aqua_tests	ChrisC1657	2022-10-02T20:33:02Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-10-02T17:06:56Z	--- license: mit --- Aqua_anime_girl_blk_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - black reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: Anime girl Aqua_animegirl_wrd_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - Waifu Research Department reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: Anime girl Aqua_CxRwMwCYViVz2JUH_blk_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - black reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: CxRwMwCYViVz2JUH Aqua_CxRwMwCYViVz2JUH_wrd_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - Waifu Research Department reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: CxRwMwCYViVz2JUH
waifu-research-department/Natsuki-Subaru	waifu-research-department	2022-10-02T20:26:18Z	0	4	null	[ "region:us" ]	null	2022-10-02T19:22:45Z	# Description Trainer: naotsue Natsuki Subaru from Re:Zero # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: SUBARU (Use this in the prompt) >Class Phrase: 1boy ![Sak](https://i.pinimg.com/originals/36/f9/c4/36f9c4b5813cb22aba08466fda544e27.png)
ericntay/stbl_clinical_bert_ft_rs9	ericntay	2022-10-02T19:02:19Z	116	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-02T18:44:01Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: stbl_clinical_bert_ft_rs9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # stbl_clinical_bert_ft_rs9 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0866 - F1: 0.9109 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2712 \| 1.0 \| 101 \| 0.0879 \| 0.8420 \| \| 0.0666 \| 2.0 \| 202 \| 0.0776 \| 0.8726 \| \| 0.031 \| 3.0 \| 303 \| 0.0630 \| 0.8923 \| \| 0.015 \| 4.0 \| 404 \| 0.0821 \| 0.8958 \| \| 0.0087 \| 5.0 \| 505 \| 0.0736 \| 0.9084 \| \| 0.0061 \| 6.0 \| 606 \| 0.0738 \| 0.9083 \| \| 0.0037 \| 7.0 \| 707 \| 0.0838 \| 0.9157 \| \| 0.0027 \| 8.0 \| 808 \| 0.0827 \| 0.9088 \| \| 0.0017 \| 9.0 \| 909 \| 0.0850 \| 0.9135 \| \| 0.0014 \| 10.0 \| 1010 \| 0.0871 \| 0.9090 \| \| 0.001 \| 11.0 \| 1111 \| 0.0868 \| 0.9094 \| \| 0.001 \| 12.0 \| 1212 \| 0.0866 \| 0.9109 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

YassineB/test_Resnet

YassineB

2022-10-04T14:34:56Z

41

0

transformers

[ "transformers", "resnet", "image-classification", "vision", "dataset:imagenet-1k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-10-04T14:10:44Z

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k ---

esc-benchmark/conformer-rnnt-common_voice

esc-benchmark

2022-10-04T14:31:55Z

1

0

nemo

[ "nemo", "esc", "en", "dataset:common_voice", "region:us" ]

null

2022-10-04T14:31:39Z

--- language: - en tags: - esc datasets: - common_voice --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \ --config_path="conf/conformer_transducer_bpe_xlarge.yaml" \ --model_name_or_path="stt_en_conformer_transducer_xlarge" \ --dataset_name="esc-benchmark/esc-datasets" \ --tokenizer_path="tokenizer" \ --vocab_size="1024" \ --max_steps="100000" \ --dataset_config_name="common_voice" \ --output_dir="./" \ --run_name="conformer-rnnt-common-voice" \ --wandb_project="rnnt" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="4" \ --logging_steps="50" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --save_strategy="steps" \ --save_steps="20000" \ --evaluation_strategy="steps" \ --eval_steps="20000" \ --report_to="wandb" \ --preprocessing_num_workers="4" \ --fused_batch_size="4" \ --length_column_name="input_lengths" \ --max_eval_duration_in_seconds="20" \ --fuse_loss_wer \ --group_by_length \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --use_auth_token ```

esc-benchmark/whisper-aed-switchboard

esc-benchmark

2022-10-04T14:24:16Z

0

null

[ "esc", "en", "dataset:switchboard", "region:us" ]

null

2022-10-04T14:23:58Z

--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="switchboard" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-switchboard" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-switchboard" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```

esc-benchmark/whisper-aed-ami

esc-benchmark

2022-10-04T14:17:38Z

0

null

[ "esc", "en", "dataset:ami", "region:us" ]

null

2022-10-04T14:17:20Z

--- language: - en tags: - esc datasets: - ami --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="ami" \ --max_steps="2500" \ --output_dir="./" \ --run_name="whisper-ami" \ --dropout_rate="0.1" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="500" \ --save_strategy="steps" \ --save_steps="500" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```

esc-benchmark/whisper-aed-earnings22

esc-benchmark

2022-10-04T14:14:02Z

0

null

[ "esc", "en", "dataset:earnings22", "region:us" ]

null

2022-10-04T14:13:44Z

--- language: - en tags: - esc datasets: - earnings22 --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="earnings22" \ --max_steps="2500" \ --output_dir="./" \ --run_name="whisper-earnings22" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="500" \ --save_strategy="steps" \ --save_steps="500" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```

esc-benchmark/whisper-aed-gigaspeech

esc-benchmark

2022-10-04T14:06:02Z

0

null

[ "esc", "en", "dataset:gigaspeech", "region:us" ]

null

2022-10-04T14:05:45Z

--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="gigaspeech" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-gigaspeech" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```

melll-uff/bertweetbr

melll-uff

2022-10-04T14:00:33Z

379

10

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "pt", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-29T21:10:30Z

--- language: pt license: apache-2.0 --- # <a name="introduction"></a> BERTweet.BR: A Pre-Trained Language Model for Tweets in Portuguese Having the same architecture of [BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet) we trained our model from scratch following [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) pre-training procedure on a corpus of approximately 9GB containing 100M Portuguese Tweets. ## Usage ### Normalized Inputs ```python import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) # INPUT TWEETS ALREADY NORMALIZED! inputs = [ "Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:", "Que jogo ontem @USER :mãos_juntas:", "Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL"] encoded_inputs = tokenizer(inputs, return_tensors="pt", padding=True) with torch.no_grad(): last_hidden_states = model(**encoded_inputs) # CLS Token of last hidden states. Shape: (number of input sentences, hidden sizeof the model) last_hidden_states[0][:,0,:] tensor([[-0.1430, -0.1325, 0.1595, ..., -0.0802, -0.0153, -0.1358], [-0.0108, 0.1415, 0.0695, ..., 0.1420, 0.1153, -0.0176], [-0.1854, 0.1866, 0.3163, ..., -0.2117, 0.2123, -0.1907]]) ``` ### Normalize raw input Tweets ```python from emoji import demojize import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('melll-uff/bertweetbr') tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=True) inputs = [ "Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim 🎵", "Que jogo ontem @cristiano 🙏", "Demojizer para Python é 👍 e está disponível em https://pypi.org/project/emoji/"] tokenizer.demojizer = lambda x: demojize(x, language='pt') [tokenizer.normalizeTweet(s) for s in inputs] # Tokenizer first normalizes tweet sentences ['Procuro um amor , que seja bom pra mim ... vou procurar , eu vou até o fim :nota_musical:', 'Que jogo ontem @USER :mãos_juntas:', 'Demojizer para Python é :polegar_para_cima: e está disponível em HTTPURL'] encoded_inputs = tokenizer(inputs, return_tensors="pt", padding=True) with torch.no_grad(): last_hidden_states = model(**encoded_inputs) # CLS Token of last hidden states. Shape: (number of input sentences, hidden sizeof the model) last_hidden_states[0][:,0,:] tensor([[-0.1430, -0.1325, 0.1595, ..., -0.0802, -0.0153, -0.1358], [-0.0108, 0.1415, 0.0695, ..., 0.1420, 0.1153, -0.0176], [-0.1854, 0.1866, 0.3163, ..., -0.2117, 0.2123, -0.1907]]) ``` ### Mask Filling with Pipeline ```python from transformers import pipeline model_name = 'melll-uff/bertweetbr' tokenizer = AutoTokenizer.from_pretrained('melll-uff/bertweetbr', normalization=False) filler_mask = pipeline("fill-mask", model=model_name, tokenizer=tokenizer) filler_mask("Rio é a <mask> cidade do Brasil.", top_k=5) # Output [{'sequence': 'Rio é a melhor cidade do Brasil.', 'score': 0.9871652126312256, 'token': 120, 'token_str': 'm e l h o r'}, {'sequence': 'Rio é a pior cidade do Brasil.', 'score': 0.005050931591540575, 'token': 316, 'token_str': 'p i o r'}, {'sequence': 'Rio é a maior cidade do Brasil.', 'score': 0.004420778248459101, 'token': 389, 'token_str': 'm a i o r'}, {'sequence': 'Rio é a minha cidade do Brasil.', 'score': 0.0021856199018657207, 'token': 38, 'token_str': 'm i n h a'}, {'sequence': 'Rio é a segunda cidade do Brasil.', 'score': 0.0002110043278662488, 'token': 667, 'token_str': 's e g u n d a'}] ```

esc-benchmark/whisper-aed-librispeech

esc-benchmark

2022-10-04T13:49:46Z

0

null

[ "esc", "en", "dataset:librispeech", "region:us" ]

null

2022-10-04T13:49:29Z

--- language: - en tags: - esc datasets: - librispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \ --model_name_or_path="medium.en" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="librispeech" \ --max_steps="5000" \ --output_dir="./" \ --run_name="whisper-librispeech" \ --wandb_project="whisper" \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="16" \ --logging_steps="25" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --report_to="wandb" \ --preprocessing_num_workers="16" \ --evaluation_strategy="steps" \ --eval_steps="1000" \ --save_strategy="steps" \ --save_steps="1000" \ --generation_max_length="224" \ --length_column_name="input_lengths" \ --gradient_checkpointing \ --group_by_length \ --freeze_encoder \ --fp16 \ --overwrite_output_dir \ --do_train \ --do_eval \ --do_predict \ --predict_with_generate \ --use_auth_token ```

esc-benchmark/wav2vec2-aed-chime4

esc-benchmark

2022-10-04T13:46:34Z

4

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:chime4", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:46:20Z

--- language: - en tags: - esc datasets: - chime4 --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="chime4" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-chime4" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="4" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="250" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.6" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

nayan06/buy-others1

nayan06

2022-10-04T13:46:25Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-04T13:46:14Z

--- 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 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  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 100 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 100, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

esc-benchmark/wav2vec2-aed-switchboard

esc-benchmark

2022-10-04T13:44:32Z

4

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:switchboard", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:44:18Z

--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="switchboard" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-switchboard" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="260" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.8" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-aed-spgispeech

esc-benchmark

2022-10-04T13:38:15Z

5

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:spgispeech", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:38:01Z

--- language: - en tags: - esc datasets: - spgispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="spgispeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-spgispeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="225" \ --final_generation_num_beams="14" \ --generation_length_penalty="1.6" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-aed-gigaspeech

esc-benchmark

2022-10-04T13:35:54Z

7

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:gigaspeech", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:35:41Z

--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="gigaspeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-gigaspeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="200" \ --final_generation_num_beams="14" \ --generation_length_penalty="1.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-aed-voxpopuli

esc-benchmark

2022-10-04T13:33:40Z

5

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:voxpopuli", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:33:27Z

--- language: - en tags: - esc datasets: - voxpopuli --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="voxpopuli" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-voxpopuli" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="1" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="10001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="225" \ --final_generation_num_beams="5" \ --generation_length_penalty="0.8" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-aed-librispeech

esc-benchmark

2022-10-04T13:27:46Z

5

0

transformers

[ "transformers", "jax", "speech-encoder-decoder", "automatic-speech-recognition", "esc", "en", "dataset:librispeech", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:27:32Z

--- language: - en tags: - esc datasets: - librispeech --- To reproduce this run, execute: ```python #!/usr/bin/env bash python run_flax_speech_recognition_seq2seq.py \ --dataset_name="esc-benchmark/esc-datasets" \ --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \ --dataset_config_name="librispeech" \ --output_dir="./" \ --wandb_name="wav2vec2-aed-librispeech" \ --wandb_project="wav2vec2-aed" \ --per_device_train_batch_size="8" \ --per_device_eval_batch_size="2" \ --learning_rate="1e-4" \ --warmup_steps="500" \ --logging_steps="25" \ --max_steps="50001" \ --eval_steps="10000" \ --save_steps="10000" \ --generation_max_length="40" \ --generation_num_beams="1" \ --final_generation_max_length="300" \ --final_generation_num_beams="12" \ --generation_length_penalty="1.6" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --predict_with_generate \ --do_eval \ --do_train \ --do_predict \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-ctc-chime4

esc-benchmark

2022-10-04T13:23:26Z

4

0

transformers

[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:chime4", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:23:19Z

--- language: - en tags: - esc datasets: - chime4 --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-chime4-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="chime4" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-chime4" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-ctc-switchboard

esc-benchmark

2022-10-04T13:22:21Z

5

0

transformers

[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:switchboard", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:22:13Z

--- language: - en tags: - esc datasets: - switchboard --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-switchboard-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="switchboard" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-switchboard" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```

esc-benchmark/wav2vec2-ctc-ami

esc-benchmark

2022-10-04T13:21:19Z

4

0

transformers

[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:ami", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:21:11Z

--- language: - en tags: - esc datasets: - ami --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-ami-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="ami" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-ami" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --hidden_dropout="0.2" \ --activation_dropout="0.2" \ --feat_proj_dropout="0.2" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```

Akshata/autotrain-person-name-validity1-1655358687

Akshata

2022-10-04T13:17:17Z

99

0

transformers

[ "transformers", "pytorch", "autotrain", "token-classification", "en", "dataset:Akshata/autotrain-data-person-name-validity1", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

token-classification

2022-10-04T13:15:05Z

--- tags: - autotrain - token-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - Akshata/autotrain-data-person-name-validity1 co2_eq_emissions: emissions: 0.015012024821802214 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1655358687 - CO2 Emissions (in grams): 0.0150 ## Validation Metrics - Loss: 0.038 - Accuracy: 0.991 - Precision: 0.000 - Recall: 0.000 - F1: 0.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Akshata/autotrain-person-name-validity1-1655358687 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Akshata/autotrain-person-name-validity1-1655358687", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

esc-benchmark/wav2vec2-ctc-gigaspeech

esc-benchmark

2022-10-04T13:15:20Z

4

0

transformers

[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "esc", "en", "dataset:gigaspeech", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T13:15:12Z

--- language: - en tags: - esc datasets: - gigaspeech --- To reproduce this run, first call `get_ctc_tokenizer.py` to train the CTC tokenizer and then execute the following command to train the CTC system: ```python #!/usr/bin/env bash python run_flax_speech_recognition_ctc.py \ --model_name_or_path="esc-benchmark/wav2vec2-ctc-pretrained" \ --tokenizer_name="wav2vec2-ctc-gigaspeech-tokenizer" \ --dataset_name="esc-benchmark/esc-datasets" \ --dataset_config_name="gigaspeech" \ --output_dir="./" \ --wandb_project="wav2vec2-ctc" \ --wandb_name="wav2vec2-ctc-gigaspeech" \ --max_steps="50000" \ --save_steps="10000" \ --eval_steps="10000" \ --learning_rate="3e-4" \ --logging_steps="25" \ --warmup_steps="5000" \ --preprocessing_num_workers="1" \ --do_train \ --do_eval \ --do_predict \ --overwrite_output_dir \ --gradient_checkpointing \ --freeze_feature_encoder \ --push_to_hub \ --use_auth_token ```

bharadwajkg/finetuning-cardiffnlp-twitter-roberta-base-sentiment

bharadwajkg

2022-10-04T12:28:23Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-04T10:47:00Z

--- tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy - f1 model-index: - name: finetuning-cardiffnlp-twitter-roberta-base-sentiment results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: sentiment split: train args: sentiment metrics: - name: Accuracy type: accuracy value: 0.7433333333333333 - name: F1 type: f1 value: 0.7418048347838402 ---  # finetuning-cardiffnlp-twitter-roberta-base-sentiment This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 2.0244 - Accuracy: 0.7433 - F1: 0.7418 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

alessandroen/asr_test

alessandroen

2022-10-04T11:10:56Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-23T10:46:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: asr_test results: [] ---  # asr_test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

bharadwajkg/finetuning-sentiment-model-3000-samples-imdb

bharadwajkg

2022-10-04T10:26:13Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-04T10:12:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8733333333333333 - name: F1 type: f1 value: 0.8741721854304636 ---  # finetuning-sentiment-model-3000-samples-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3054 - Accuracy: 0.8733 - F1: 0.8742 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

Nithiwat/mdeberta-v3-base_claim-detection

Nithiwat

2022-10-04T10:24:41Z

100

1

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "claim-detection", "en", "dataset:Nithiwat/claim-detection", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-04T07:42:30Z

--- language: - en tags: - text-classification - claim-detection license: "mit" datasets: - Nithiwat/claim-detection widget: - text: "This is the best cast iron skillet you will ever buy." - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday." - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book" ---

openai/clip-vit-large-patch14-336

openai

2022-10-04T09:41:39Z

4,489,460

223

transformers

[ "transformers", "pytorch", "tf", "clip", "zero-shot-image-classification", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]

zero-shot-image-classification

2022-04-22T14:57:43Z

--- tags: - generated_from_keras_callback widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog model-index: - name: clip-vit-large-patch14-336 results: [] ---  # clip-vit-large-patch14-336 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.21.3 - TensorFlow 2.8.2 - Tokenizers 0.12.1

mouss/autotrain-damages-1652858619

mouss

2022-10-04T09:41:15Z

191

0

transformers

[ "transformers", "pytorch", "autotrain", "vision", "image-classification", "dataset:mouss/autotrain-data-damages", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

image-classification

2022-10-04T09:39:50Z

--- tags: - autotrain - vision - image-classification datasets: - mouss/autotrain-data-damages widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.007316433431312107 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1652858619 - CO2 Emissions (in grams): 0.0073 ## Validation Metrics - Loss: 0.082 - Accuracy: 0.989 - Precision: 1.000 - Recall: 0.978 - AUC: 0.995 - F1: 0.989

GItaf/bert-base-uncased-bert-base-uncased-mc-weight1-epoch15

GItaf

2022-10-04T09:32:23Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-10-02T15:01:50Z

--- tags: - generated_from_trainer model-index: - name: bert-base-uncased-bert-base-uncased-mc-weight1-epoch15 results: [] ---  # bert-base-uncased-bert-base-uncased-mc-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.8027 - Cls loss: 3.4449 - Lm loss: 4.3556 - Cls Accuracy: 0.5706 - Cls F1: 0.5697 - Cls Precision: 0.5753 - Cls Recall: 0.5706 - Perplexity: 77.91 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:| | 6.9526 | 1.0 | 3470 | 6.3154 | 1.7748 | 4.5399 | 0.4991 | 0.4577 | 0.4421 | 0.4991 | 93.68 | | 6.0876 | 2.0 | 6940 | 6.1427 | 1.6773 | 4.4643 | 0.5545 | 0.5342 | 0.5717 | 0.5545 | 86.86 | | 5.7231 | 3.0 | 10410 | 6.0206 | 1.5955 | 4.4240 | 0.5902 | 0.5759 | 0.6020 | 0.5902 | 83.43 | | 5.3877 | 4.0 | 13880 | 5.9857 | 1.5772 | 4.4073 | 0.6092 | 0.6031 | 0.6052 | 0.6092 | 82.05 | | 5.1092 | 5.0 | 17350 | 6.3742 | 1.9981 | 4.3748 | 0.5942 | 0.5901 | 0.5964 | 0.5942 | 79.42 | | 4.8504 | 6.0 | 20820 | 6.4511 | 2.0776 | 4.3737 | 0.5890 | 0.5875 | 0.6041 | 0.5890 | 79.34 | | 4.6369 | 7.0 | 24290 | 6.9857 | 2.6268 | 4.3571 | 0.5827 | 0.5796 | 0.5979 | 0.5827 | 78.03 | | 4.4667 | 8.0 | 27760 | 6.9550 | 2.6075 | 4.3458 | 0.5833 | 0.5831 | 0.5904 | 0.5833 | 77.16 | | 4.3127 | 9.0 | 31230 | 7.2041 | 2.8518 | 4.3504 | 0.5902 | 0.5856 | 0.5935 | 0.5902 | 77.51 | | 4.1777 | 10.0 | 34700 | 7.4233 | 3.0746 | 4.3467 | 0.5793 | 0.5770 | 0.5829 | 0.5793 | 77.22 | | 4.0871 | 11.0 | 38170 | 7.4997 | 3.1488 | 4.3489 | 0.5746 | 0.5749 | 0.5853 | 0.5746 | 77.39 | | 3.9991 | 12.0 | 41640 | 7.6636 | 3.3113 | 4.3502 | 0.5602 | 0.5605 | 0.5676 | 0.5602 | 77.49 | | 3.9461 | 13.0 | 45110 | 7.6065 | 3.2514 | 4.3530 | 0.5695 | 0.5690 | 0.5738 | 0.5695 | 77.71 | | 3.9013 | 14.0 | 48580 | 7.7562 | 3.4017 | 4.3523 | 0.5787 | 0.5785 | 0.5823 | 0.5787 | 77.65 | | 3.8731 | 15.0 | 52050 | 7.8027 | 3.4449 | 4.3556 | 0.5706 | 0.5697 | 0.5753 | 0.5706 | 77.91 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

flair/ner-multi-fast

flair

2022-10-04T09:19:01Z

198

6

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "de", "nl", "es", "dataset:conll2003", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- tags: - flair - token-classification - sequence-tagger-model language: - en - de - nl - es datasets: - conll2003 widget: - text: "George Washington ging nach Washington" --- ## 4-Language NER in Flair (English, German, Dutch and Spanish) This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French. F1-Score: **91,51** (CoNLL-03 English), **85,72** (CoNLL-03 German revised), **86,22** (CoNLL-03 Dutch), **85,78** (CoNLL-03 Spanish) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | PER | person name | | LOC | location name | | ORG | organization name | | MISC | other name | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-multi-fast") # make example sentence in any of the four languages sentence = Sentence("George Washington ging nach Washington") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [1,2]: "George Washington" [− Labels: PER (0.9977)] Span [5]: "Washington" [− Labels: LOC (0.9895)] ``` So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the multi-language corpus corpus: Corpus = MultiCorpus([ CONLL_03(), # English corpus CONLL_03_GERMAN(), # German corpus CONLL_03_DUTCH(), # Dutch corpus CONLL_03_SPANISH(), # Spanish corpus ]) # 2. what tag do we want to predict? tag_type = 'ner' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # GloVe embeddings WordEmbeddings('glove'), # FastText embeddings WordEmbeddings('de'), # contextual string embeddings, forward FlairEmbeddings('multi-forward-fast'), # contextual string embeddings, backward FlairEmbeddings('multi-backward-fast'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-multi-fast', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following papers when using this model. ``` @misc{akbik2019multilingual, title={Multilingual sequence labeling with one model}, author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland} booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop}, year = {2019} } ``` ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ```

sd-concepts-library/crb-surrealz

sd-concepts-library

2022-10-04T08:59:07Z

0

1

null

[ "license:mit", "region:us" ]

null

2022-10-04T08:59:02Z

--- license: mit --- ### crb-surrealz on Stable Diffusion This is the `<crbsurreal>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<crbsurreal> 0](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/2.jpeg) ![<crbsurreal> 1](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/4.jpeg) ![<crbsurreal> 2](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/11.jpeg) ![<crbsurreal> 3](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/1.jpeg) ![<crbsurreal> 4](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/9.jpeg) ![<crbsurreal> 5](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/8.jpeg) ![<crbsurreal> 6](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/3.jpeg) ![<crbsurreal> 7](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/0.jpeg) ![<crbsurreal> 8](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/7.jpeg) ![<crbsurreal> 9](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/5.jpeg) ![<crbsurreal> 10](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/10.jpeg) ![<crbsurreal> 11](https://huggingface.co/sd-concepts-library/crb-surrealz/resolve/main/concept_images/6.jpeg)

eunyounglee/wav2vec_korean

eunyounglee

2022-10-04T05:53:52Z

16

1

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-04T03:25:17Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec_korean results: [] ---  # wav2vec_korean This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.13.0

farzeen/ppo-LunarLander-v2

farzeen

2022-10-04T05:23:46Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-04T05:23:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 249.94 +/- 23.25 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

anas-awadalla/gpt2-medium-span-head-finetuned-squad

anas-awadalla

2022-10-04T05:08:07Z

475

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2022-09-26T20:15:16Z

--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-medium-span-head-finetuned-squad results: [] ---  # gpt2-medium-span-head-finetuned-squad This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the squad 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: 3e-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: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

Hasanmurad/banglabert-bert-finetuned-ner

Hasanmurad

2022-10-04T05:07:40Z

89

0

transformers

[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-04T04:43:17Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: banglabert-bert-finetuned-ner results: [] ---  # banglabert-bert-finetuned-ner This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9526 - Precision: 0.0143 - Recall: 0.0769 - F1: 0.0241 - Accuracy: 0.0143 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 1 | 2.0085 | 0.0143 | 0.0769 | 0.0241 | 0.0143 | | No log | 2.0 | 2 | 1.9711 | 0.0143 | 0.0769 | 0.0241 | 0.0143 | | No log | 3.0 | 3 | 1.9526 | 0.0143 | 0.0769 | 0.0241 | 0.0143 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

KES/ENG-TEC

KES

2022-10-04T05:04:39Z

113

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "Trinidadian Creole", "Caribbean dialect", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-02T14:47:01Z

--- tags: - text2text-generation - Trinidadian Creole - Caribbean dialect license: apache-2.0 --- # Standard English to Trinidad English Creole Translator This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset for translation of English to Trinidad English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/). ___ # Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/ENG-TEC") model = AutoModelForSeq2SeqLM.from_pretrained("KES/ENG-TEC") text = "Where are you going now?" inputs = tokenizer("eng:"+text, truncation=True, return_tensors='pt') output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True) translation=tokenizer.batch_decode(output, skip_special_tokens=True) print("".join(translation)) #translation: Weh yuh going now. ``` ___

jon-fernandes/vit-base-patch16-224-finetuned-flower

jon-fernandes

2022-10-04T05:00:26Z

216

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-10-04T04:51:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] ---  # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/liminal-spaces-2-0

sd-concepts-library

2022-10-04T04:01:31Z

0

4

null

[ "license:mit", "region:us" ]

null

2022-10-04T04:01:19Z

--- license: mit --- ### Liminal spaces 2.0 on Stable Diffusion This is the `liminal image` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![liminal image 0](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/2.jpeg) ![liminal image 1](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/4.jpeg) ![liminal image 2](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/11.jpeg) ![liminal image 3](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/15.jpeg) ![liminal image 4](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/16.jpeg) ![liminal image 5](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/1.jpeg) ![liminal image 6](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/9.jpeg) ![liminal image 7](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/8.jpeg) ![liminal image 8](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/14.jpeg) ![liminal image 9](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/3.jpeg) ![liminal image 10](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/0.jpeg) ![liminal image 11](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/19.jpeg) ![liminal image 12](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/7.jpeg) ![liminal image 13](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/13.jpeg) ![liminal image 14](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/17.jpeg) ![liminal image 15](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/18.jpeg) ![liminal image 16](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/5.jpeg) ![liminal image 17](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/12.jpeg) ![liminal image 18](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/10.jpeg) ![liminal image 19](https://huggingface.co/sd-concepts-library/liminal-spaces-2-0/resolve/main/concept_images/6.jpeg)

nousr/alien-diffusion

nousr

2022-10-04T03:47:20Z

0

2

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-10-04T03:44:58Z

--- license: creativeml-openrail-m ---

huggingtweets/whoisaddison

huggingtweets

2022-10-04T03:10:08Z

96

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-05-16T23:20:50Z

--- 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/1570620381324578816/UG-qT7hg_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">Addison Rae</div> <div style="text-align: center; font-size: 14px;">@whoisaddison</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 Addison Rae. | Data | Addison Rae | | --- | --- | | Tweets downloaded | 3204 | | Retweets | 473 | | Short tweets | 957 | | Tweets kept | 1774 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6p4jofae/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 @whoisaddison's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2/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/whoisaddison') 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)

GItaf/gpt2-gpt2-mc-weight0.25-epoch15

GItaf

2022-10-04T02:57:49Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-03T14:42:06Z

--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight0.25-epoch15 results: [] ---  # gpt2-gpt2-mc-weight0.25-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8155 - Cls loss: 3.4105 - Lm loss: 3.9623 - Cls Accuracy: 0.6104 - Cls F1: 0.6054 - Cls Precision: 0.6110 - Cls Recall: 0.6104 - Perplexity: 52.58 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:| | 4.6818 | 1.0 | 3470 | 4.4437 | 1.6250 | 4.0374 | 0.5274 | 0.4958 | 0.5329 | 0.5274 | 56.68 | | 4.3828 | 2.0 | 6940 | 4.3628 | 1.4528 | 3.9994 | 0.6144 | 0.6088 | 0.6345 | 0.6144 | 54.56 | | 4.2523 | 3.0 | 10410 | 4.3820 | 1.5899 | 3.9842 | 0.6092 | 0.6025 | 0.6382 | 0.6092 | 53.74 | | 4.1442 | 4.0 | 13880 | 4.3954 | 1.6755 | 3.9763 | 0.6063 | 0.6010 | 0.6121 | 0.6063 | 53.32 | | 4.0385 | 5.0 | 17350 | 4.4675 | 2.0051 | 3.9659 | 0.6150 | 0.6105 | 0.6194 | 0.6150 | 52.77 | | 3.9513 | 6.0 | 20820 | 4.5223 | 2.2257 | 3.9654 | 0.6115 | 0.6049 | 0.6233 | 0.6115 | 52.74 | | 3.8877 | 7.0 | 24290 | 4.5904 | 2.5003 | 3.9649 | 0.6012 | 0.5956 | 0.6049 | 0.6012 | 52.71 | | 3.8367 | 8.0 | 27760 | 4.6320 | 2.6812 | 3.9612 | 0.6121 | 0.6061 | 0.6154 | 0.6121 | 52.52 | | 3.7991 | 9.0 | 31230 | 4.6735 | 2.8534 | 3.9596 | 0.6104 | 0.6059 | 0.6139 | 0.6104 | 52.44 | | 3.7697 | 10.0 | 34700 | 4.7126 | 3.0044 | 3.9610 | 0.6104 | 0.6063 | 0.6122 | 0.6104 | 52.51 | | 3.7457 | 11.0 | 38170 | 4.7607 | 3.1961 | 3.9612 | 0.6133 | 0.6072 | 0.6182 | 0.6133 | 52.52 | | 3.7265 | 12.0 | 41640 | 4.7927 | 3.3216 | 3.9617 | 0.6006 | 0.5951 | 0.6036 | 0.6006 | 52.55 | | 3.7129 | 13.0 | 45110 | 4.7983 | 3.3431 | 3.9620 | 0.6104 | 0.6039 | 0.6133 | 0.6104 | 52.56 | | 3.7016 | 14.0 | 48580 | 4.8061 | 3.3774 | 3.9612 | 0.6121 | 0.6059 | 0.6124 | 0.6121 | 52.52 | | 3.6956 | 15.0 | 52050 | 4.8155 | 3.4105 | 3.9623 | 0.6104 | 0.6054 | 0.6110 | 0.6104 | 52.58 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

GItaf/gpt2-gpt2-mc-weight1-epoch15

GItaf

2022-10-04T02:56:34Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-02T15:10:31Z

--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight1-epoch15 results: [] ---  # gpt2-gpt2-mc-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.6876 - Cls loss: 3.7214 - Lm loss: 3.9640 - Cls Accuracy: 0.6040 - Cls F1: 0.5981 - Cls Precision: 0.6050 - Cls Recall: 0.6040 - Perplexity: 52.67 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:| | 6.3006 | 1.0 | 3470 | 5.7550 | 1.7137 | 4.0417 | 0.5326 | 0.4990 | 0.4983 | 0.5326 | 56.92 | | 5.5103 | 2.0 | 6940 | 5.5608 | 1.5450 | 4.0149 | 0.6075 | 0.6009 | 0.6160 | 0.6075 | 55.42 | | 5.2167 | 3.0 | 10410 | 5.7608 | 1.7609 | 3.9988 | 0.5977 | 0.5917 | 0.6161 | 0.5977 | 54.53 | | 4.9916 | 4.0 | 13880 | 5.8042 | 1.8106 | 3.9925 | 0.6035 | 0.5979 | 0.6063 | 0.6035 | 54.19 | | 4.7224 | 5.0 | 17350 | 6.0519 | 2.0699 | 3.9807 | 0.6144 | 0.6100 | 0.6152 | 0.6144 | 53.56 | | 4.4802 | 6.0 | 20820 | 6.3862 | 2.4050 | 3.9798 | 0.5948 | 0.5883 | 0.6071 | 0.5948 | 53.51 | | 4.2926 | 7.0 | 24290 | 6.5793 | 2.6045 | 3.9733 | 0.5890 | 0.5819 | 0.5940 | 0.5890 | 53.16 | | 4.1321 | 8.0 | 27760 | 6.8574 | 2.8865 | 3.9692 | 0.5977 | 0.5937 | 0.6047 | 0.5977 | 52.94 | | 4.022 | 9.0 | 31230 | 7.1316 | 3.1624 | 3.9673 | 0.5948 | 0.5882 | 0.5980 | 0.5948 | 52.84 | | 3.9255 | 10.0 | 34700 | 7.1732 | 3.2049 | 3.9664 | 0.6017 | 0.5985 | 0.6009 | 0.6017 | 52.79 | | 3.8619 | 11.0 | 38170 | 7.3778 | 3.4104 | 3.9653 | 0.5994 | 0.5929 | 0.5994 | 0.5994 | 52.74 | | 3.8141 | 12.0 | 41640 | 7.5111 | 3.5452 | 3.9638 | 0.5873 | 0.5834 | 0.5916 | 0.5873 | 52.66 | | 3.7859 | 13.0 | 45110 | 7.6660 | 3.6998 | 3.9640 | 0.5960 | 0.5889 | 0.5976 | 0.5960 | 52.67 | | 3.7628 | 14.0 | 48580 | 7.6558 | 3.6900 | 3.9636 | 0.5954 | 0.5899 | 0.5969 | 0.5954 | 52.65 | | 3.7539 | 15.0 | 52050 | 7.6876 | 3.7214 | 3.9640 | 0.6040 | 0.5981 | 0.6050 | 0.6040 | 52.67 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

sd-concepts-library/chungus-poodl-pet

sd-concepts-library

2022-10-04T02:56:02Z

0

null

[ "license:mit", "region:us" ]

null

2022-10-04T02:55:50Z

--- license: mit --- ### Chungus Poodl Pet on Stable Diffusion This is the `<poodl-chungus-big>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<poodl-chungus-big> 0](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/52.jpeg) ![<poodl-chungus-big> 1](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/55.jpeg) ![<poodl-chungus-big> 2](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/44.jpeg) ![<poodl-chungus-big> 3](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/2.jpeg) ![<poodl-chungus-big> 4](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/27.jpeg) ![<poodl-chungus-big> 5](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/43.jpeg) ![<poodl-chungus-big> 6](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/47.jpeg) ![<poodl-chungus-big> 7](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/4.jpeg) ![<poodl-chungus-big> 8](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/32.jpeg) ![<poodl-chungus-big> 9](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/46.jpeg) ![<poodl-chungus-big> 10](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/11.jpeg) ![<poodl-chungus-big> 11](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/45.jpeg) ![<poodl-chungus-big> 12](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/15.jpeg) ![<poodl-chungus-big> 13](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/16.jpeg) ![<poodl-chungus-big> 14](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/23.jpeg) ![<poodl-chungus-big> 15](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/1.jpeg) ![<poodl-chungus-big> 16](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/9.jpeg) ![<poodl-chungus-big> 17](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/36.jpeg) ![<poodl-chungus-big> 18](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/39.jpeg) ![<poodl-chungus-big> 19](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/40.jpeg) ![<poodl-chungus-big> 20](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/26.jpeg) ![<poodl-chungus-big> 21](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/8.jpeg) ![<poodl-chungus-big> 22](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/38.jpeg) ![<poodl-chungus-big> 23](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/42.jpeg) ![<poodl-chungus-big> 24](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/22.jpeg) ![<poodl-chungus-big> 25](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/35.jpeg) ![<poodl-chungus-big> 26](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/33.jpeg) ![<poodl-chungus-big> 27](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/49.jpeg) ![<poodl-chungus-big> 28](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/14.jpeg) ![<poodl-chungus-big> 29](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/3.jpeg) ![<poodl-chungus-big> 30](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/51.jpeg) ![<poodl-chungus-big> 31](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/37.jpeg) ![<poodl-chungus-big> 32](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/30.jpeg) ![<poodl-chungus-big> 33](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/0.jpeg) ![<poodl-chungus-big> 34](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/19.jpeg) ![<poodl-chungus-big> 35](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/54.jpeg) ![<poodl-chungus-big> 36](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/24.jpeg) ![<poodl-chungus-big> 37](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/7.jpeg) ![<poodl-chungus-big> 38](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/48.jpeg) ![<poodl-chungus-big> 39](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/21.jpeg) ![<poodl-chungus-big> 40](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/50.jpeg) ![<poodl-chungus-big> 41](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/25.jpeg) ![<poodl-chungus-big> 42](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/13.jpeg) ![<poodl-chungus-big> 43](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/17.jpeg) ![<poodl-chungus-big> 44](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/31.jpeg) ![<poodl-chungus-big> 45](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/18.jpeg) ![<poodl-chungus-big> 46](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/5.jpeg) ![<poodl-chungus-big> 47](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/29.jpeg) ![<poodl-chungus-big> 48](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/34.jpeg) ![<poodl-chungus-big> 49](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/28.jpeg) ![<poodl-chungus-big> 50](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/41.jpeg) ![<poodl-chungus-big> 51](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/12.jpeg) ![<poodl-chungus-big> 52](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/20.jpeg) ![<poodl-chungus-big> 53](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/10.jpeg) ![<poodl-chungus-big> 54](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/53.jpeg) ![<poodl-chungus-big> 55](https://huggingface.co/sd-concepts-library/chungus-poodl-pet/resolve/main/concept_images/6.jpeg)

anas-awadalla/gpt2-large-span-head-finetuned-squad

anas-awadalla

2022-10-04T01:27:34Z

537

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2022-09-26T20:01:28Z

--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-large-span-head-finetuned-squad results: [] ---  # gpt2-large-span-head-finetuned-squad This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the squad 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

aaronryanmanuel/distilbert-base-uncased-finetuned-cola

aaronryanmanuel

2022-10-04T01:21:22Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T15:38:32Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5229395497643199 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8233 - Matthews Correlation: 0.5229 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5367 | 1.0 | 535 | 0.5657 | 0.3638 | | 0.3692 | 2.0 | 1070 | 0.5291 | 0.4912 | | 0.2503 | 3.0 | 1605 | 0.5442 | 0.5038 | | 0.1895 | 4.0 | 2140 | 0.7376 | 0.5112 | | 0.1363 | 5.0 | 2675 | 0.8233 | 0.5229 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

waifu-research-department/Zero-Two

waifu-research-department

2022-10-04T00:21:52Z

0

3

null

[ "region:us" ]

null

2022-10-03T01:35:48Z

# Description Trainer: naotsue Zero Two from Darling in the FranXX # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: ZERO-TWO (Use this in the prompt) >Class Phrase: 1girl ![Sak](https://www.enjpg.com/img/2020/zero-two-57.jpg)

waifu-research-department/Ayanokouji-Kiyotaka

waifu-research-department

2022-10-04T00:20:41Z

0

2

null

[ "region:us" ]

null

2022-10-03T01:35:12Z

# Description Trainer: naotsue Ayanokouji Kiyotaka from Classroom of the Elite # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: AYANOKOUJI (Use this in the prompt) >Class Phrase: 1boy ![Sak](https://animevania.com/wp-content/uploads/2022/07/classroom-of-the-elite-kiyotaka-ayanokoji-4k-wallpaper-uhdpaper.com-557@[email protected])

grantsl/distilbert-base-uncased-finetuned-emotion-2

grantsl

2022-10-04T00:16:54Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T23:32:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion-2 results: [] ---  # distilbert-base-uncased-finetuned-emotion-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.8433 - F1: 0.8433 ## 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: 128 - eval_batch_size: 128 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4095 | 1.0 | 875 | 0.3667 | 0.8353 | 0.8351 | | 0.3348 | 2.0 | 1750 | 0.3608 | 0.8433 | 0.8433 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

MoseliMotsoehli/FishNet

MoseliMotsoehli

2022-10-03T23:30:15Z

0

1

null

[ "license:openrail", "region:us" ]

null

2022-10-02T20:37:48Z

--- license: openrail --- # <span style="color:blue">FishNet: AI For Fish Stock Estimation</span> The attached model was trained on a 63000 dataset of fish images belonging to 163 species. First, we trained a detectron2 model to detect and segment fish and fiduciary markers on a board. The detectron2 model was written in PyTorch, and the final classifier is a ResNet50 keras model. Below is an example of how to use the 2 models. ## Packages ## Load Models ## load an image and transform ## run the segmentation model ## visualize ## classify the fish

faisito/distilbert-base-uncased-finetuned-emotion

faisito

2022-10-03T22:19:00Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T21:45:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.925520268497019 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9255 - F1: 0.9255 ## 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: 64 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8237 | 1.0 | 250 | 0.3205 | 0.9045 | 0.9002 | | 0.2539 | 2.0 | 500 | 0.2170 | 0.9255 | 0.9255 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

suresh-subramanian/autotrain-fake-news-1649058542

suresh-subramanian

2022-10-03T22:13:59Z

103

0

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T22:08:00Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 12.699762619910537 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058542 - CO2 Emissions (in grams): 12.6998 ## Validation Metrics - Loss: 0.624 - Accuracy: 0.637 - Precision: 1.000 - Recall: 0.020 - AUC: 0.652 - F1: 0.039 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058542 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058542", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

suresh-subramanian/autotrain-fake-news-1649058539

suresh-subramanian

2022-10-03T22:12:00Z

102

0

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T22:07:19Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 0.040297872306469855 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058539 - CO2 Emissions (in grams): 0.0403 ## Validation Metrics - Loss: 0.478 - Accuracy: 0.779 - Precision: 0.814 - Recall: 0.520 - AUC: 0.881 - F1: 0.635 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058539 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058539", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

suresh-subramanian/autotrain-fake-news-1649058538

suresh-subramanian

2022-10-03T22:11:11Z

100

0

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:suresh-subramanian/autotrain-data-fake-news", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T22:07:12Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - suresh-subramanian/autotrain-data-fake-news co2_eq_emissions: emissions: 0.04097854185629584 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1649058538 - CO2 Emissions (in grams): 0.0410 ## Validation Metrics - Loss: 0.387 - Accuracy: 0.815 - Precision: 0.760 - Recall: 0.730 - AUC: 0.902 - F1: 0.745 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/suresh-subramanian/autotrain-fake-news-1649058538 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("suresh-subramanian/autotrain-fake-news-1649058538", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

allenai/scibert_scivocab_uncased

allenai

2022-10-03T22:06:12Z

691,053

137

transformers

[ "transformers", "pytorch", "jax", "bert", "en", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en --- # SciBERT This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. Available models include: * `scibert_scivocab_cased` * `scibert_scivocab_uncased` The original repo can be found [here](https://github.com/allenai/scibert). If using these models, please cite the following paper: ``` @inproceedings{beltagy-etal-2019-scibert, title = "SciBERT: A Pretrained Language Model for Scientific Text", author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", booktitle = "EMNLP", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1371" } ```

allenai/scibert_scivocab_cased

allenai

2022-10-03T22:04:46Z

6,859

14

transformers

[ "transformers", "pytorch", "jax", "bert", "en", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en --- # SciBERT This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. Available models include: * `scibert_scivocab_cased` * `scibert_scivocab_uncased` The original repo can be found [here](https://github.com/allenai/scibert). If using these models, please cite the following paper: ``` @inproceedings{beltagy-etal-2019-scibert, title = "SciBERT: A Pretrained Language Model for Scientific Text", author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", booktitle = "EMNLP", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1371" } ```

allenai/aspire-biencoder-compsci-spec

allenai

2022-10-03T22:03:49Z

116

1

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-04-23T14:15:21Z

--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `Specter-CoCite_Spec` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is **initialized with the SPECTER model**. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the base encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-compsci-spec-full.zip`](https://drive.google.com/file/d/1AHtzyEpyn7DeFYOdt86ik4n0tGaG5kMC/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in **computer science** scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from computer science, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Performance here is reported on CSFCube (computer science/English). This is detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). CSFCube presents a finer-grained query via selected sentences in a query abstract based on which a finer-grained retrieval must be made from candidate abstracts. The bi-encoder above ignores the finer grained query sentences and uses the whole abstract - this presents a baseline in the paper. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-compsci-spec` (and `aspire-biencoder-compsci-spec-full`) is compared against `allenai/specter`. `aspire-biencoder-compsci-spec-full`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-compsci-spec` and `aspire-biencoder-compsci-spec-full` are the single best run among the 3 re-runs. | | CSFCube aggregated | CSFCube aggregated| |--------------------------------------------:|:---------:|:-------:| | | MAP | NDCG%20 | | `specter` | 34.23 | 53.28 | | `aspire-biencoder-compsci-spec-full`<sup>*</sup> | 37.90 | 58.16 | | `aspire-biencoder-compsci-spec` | 37.17 | 57.91 | | `aspire-biencoder-compsci-spec-full` | 37.67 | 59.26 | **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-biomed-scib`](https://huggingface.co/allenai/aspire-biencoder-biomed-scib): If you wanted to run on biomedical papers.

allenai/aspire-biencoder-biomed-spec

allenai

2022-10-03T22:03:21Z

117

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-04-23T14:14:35Z

--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `Specter-CoCite_Spec` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is **initialized with the SPECTER encoder**. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the base encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-spec-full.zip`](https://drive.google.com/file/d/1MDCv9Fc33eP015HTWKi50WYXixh72h5c/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 1e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in **biomedical** scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-spec` (and `aspire-biencoder-biomed-spec-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-spec-full`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-spec` and `aspire-biencoder-biomed-spec-full` are the single best run among the 3 re-runs. | | TRECCOVID | TRECCOVID | RELISH | RELISH | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| | | MAP | NDCG%20 | MAP | NDCG%20 | | `specter` | 28.24 | 59.28 | 60.62| 77.20 | | `aspire-biencoder-biomed-spec-full`<sup>*</sup> | 28.59 | 60.07 | 61.43| 77.96 | | `aspire-biencoder-biomed-spec` | 26.07 | 54.89 | 61.47| 78.34 | | `aspire-biencoder-biomed-spec-full` | 28.87 | 60.47 | 61.69| 78.22 | Note that the absence of linear mixing parameters in the `aspire-biencoder-biomed-spec` hurts performance substantially compared to `aspire-biencoder-biomed-spec-full` in TRECCOVID - this dataset contains a larger candidate set than RELISH (~9000 vs 60). Consider the more performant Alternative models below for usage. **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-scib`](https://huggingface.co/allenai/aspire-biencoder-biomed-scib): This is an alternative bi-encoder model identical to the above model, except that it is initialized with SciBERT instead of SPECTER. The above model underperforms this model, `allenai/aspire-biencoder-biomed-scib` (even better, `aspire-biencoder-biomed-scib-full`) is recommended for use.

anas-awadalla/gpt2-span-head-finetuned-squad

anas-awadalla

2022-10-03T21:37:18Z

45

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "question-answering", "generated_from_trainer", "dataset:squad", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2022-10-03T13:59:42Z

--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: gpt2-span-head-finetuned-squad results: [] ---  # gpt2-span-head-finetuned-squad This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the squad 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: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

allenai/longformer-base-4096-extra.pos.embd.only

allenai

2022-10-03T21:21:08Z

341

1

transformers

[ "transformers", "pytorch", "tf", "longformer", "en", "arxiv:2004.05150", "endpoints_compatible", "region:us" ]

null

2022-03-02T23:29:05Z

--- language: en --- # longformer-base-4096-extra.pos.embd.only This model is similar to `longformer-base-4096` but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings. ### Citing If you use `Longformer` in your research, please cite [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150). ``` @article{Beltagy2020Longformer, title={Longformer: The Long-Document Transformer}, author={Iz Beltagy and Matthew E. Peters and Arman Cohan}, journal={arXiv:2004.05150}, year={2020}, } ``` `Longformer` is an open-source project developed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

ryyyyyy/1

ryyyyyy

2022-10-03T21:21:00Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-10-03T21:21:00Z

--- license: creativeml-openrail-m ---

Hitmewmusic/First

Hitmewmusic

2022-10-03T21:20:52Z

0

null

[ "region:us" ]

null

2022-10-03T21:20:38Z

git clone https://github.com/sd-webui/stable-diffusion-webui.git

allenai/bidaf-elmo

allenai

2022-10-03T21:18:47Z

27

12

allennlp

[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: en tags: - allennlp - question-answering --- This is an implementation of the BiDAF model with ELMo embeddings. The basic layout is pretty simple: encode words as a combination of word embeddings and a character-level encoder, pass the word representations through a bi-LSTM/GRU, use a matrix of attentions to put question information into the passage word representations (this is the only part that is at all non-standard), pass this through another few layers of bi-LSTMs/GRUs, and do a softmax over span start and span end. CAVEATS: ------ This model is based on ELMo. ELMo is not deterministic, meaning that you will see slight differences every time you run it. Also, ELMo likes to be warmed up, so we recommend processing dummy input before processing real workloads with it.

allenai/aspire-contextualsentence-multim-biomed

allenai

2022-10-03T21:17:27Z

132

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-04-23T14:15:56Z

--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `tsAspire` and represents the papers proposed multi-vector model for fine-grained scientific document similarity. ## Model Card ### Model description This model is a BERT based multi-vector model trained for fine-grained similarity of biomedical papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - the whole title and abstract are encoded with cross-attention in the encoder block before obtaining sentence embeddings. The model is trained by minimizing an Wasserstein/Earth Movers Distance between sentence vectors for a pair of documents - in the process also learning a sparse alignment between sentences in both documents. Test time behavior ranks documents based on the Wasserstein Distance between all sentences of documents or a set of query sentences and a candidate documents sentences. ### Training data The model is trained on pairs of co-cited papers with their sentences aligned by the co-citation context in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model, negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers. For example - the papers in brackets below are all co-cited and each pair of papers would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for fine-grained document similarity tasks in **biomedical** scientific text using multiple vectors per document. The model allows _multiple_ fine grained sentence-to-sentence similarities between documents. The model is well suited to an aspect conditional task formulation where a query might consist of sentence_s_ in a query document and candidates must be retrieved along the specified sentences. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as document or sentence level classification. Since the training data comes primarily from biomedical, performance on other domains may be poorer. ### How to use This model can be used via the `transformers` library, and some additional code to compute contextual sentence vectors and to make multiple matches using optimal transport. View example usage and sample document matches in the model github repo: [`examples/demo-contextualsentence-multim.ipynb`](https://github.com/allenai/aspire/blob/main/examples/demo-contextualsentence-multim.ipynb) ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. In using this model we rank documents by the Wasserstein distance between the query sentences and a candidates sentences. ### Evaluation results The released model `aspire-contextualsentence-multim-biomed` is compared against `allenai/specter`. `aspire-contextualsentence-multim-biomed`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-contextualsentence-multim-biomed` is the single best run among the 3 re-runs. | | TRECCOVID | TRECCOVID | RELISH | RELISH | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| | | MAP | NDCG%20 | MAP | NDCG%20 | | `specter` | 28.24 | 59.28 | 60.62 | 77.20 | | `aspire-contextualsentence-multim-biomed`<sup>*</sup> | 30.92 | 62.23 | 62.57 | 78.95 | | `aspire-contextualsentence-multim-biomed` | 31.25 | 62.99 | 62.24 | 78.65 | **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-contextualsentence-multim-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-multim-compsci): If you wanted to run on computer science papers and want to use a model trained to match _multiple_ sentences between documents. [`aspire-contextualsentence-singlem-biomed`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-biomed): If you wanted to run on biomedical papers and want to use a model trained to match _single_ sentences between documents. [`aspire-contextualsentence-singlem-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-compsci): If you wanted to run on computer science papers and want to use a model trained to match _single_ sentences between documents.

allenai/aspire-biencoder-biomed-scib

allenai

2022-10-03T21:12:46Z

122

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:2111.08366", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-04-23T13:47:04Z

--- language: en license: apache-2.0 --- ## Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://github.com/allenai/aspire **Note**: In the context of the paper, this model is referred to as `Specter-CoCite_Scib` and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the [`allenai/specter`](https://github.com/allenai/specter) model but is trained on co-citation data instead of citation data. ## Model Card ### Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is **initialized with the SciBert model**. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS token at every layer of the SciBert encoder. These scalar mix parameters can be important for performance in some datasets. Importantly, these scalar mix weights are not included as part of this HF model, if you wish to use these parameters please download the full model at: [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing). ### Training data The model is trained on pairs of co-cited papers in a contrastive learning setup. The model is trained on 1.2 million biomedical paper pairs. In training the model negative examples for the contrastive loss are obtained as random in-batch negatives. Co-citations are obtained from the full text of papers, for example - the papers in brackets below are all co-cited and each pairs title and abstracts would be used as a training pair: > The idea of distant supervision has been proposed and used widely in Relation Extraction (Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) , where the source of labels is an external knowledge base. ### Training procedure The model was trained with the Adam Optimizer and a learning rate of 2e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. ### Intended uses & limitations This model is trained for document similarity tasks in **biomedical** scientific text using a single vector per document. Here, the documents are the title and abstract of a paper. With appropriate fine-tuning the model can also be used for other tasks such as classification. Since the training data comes primarily from biomedicine, performance on other domains may be poorer. ### How to use Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite ### Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. We rank documents by the L2 distance between the query and candidate documents. ### Evaluation results The released model `aspire-biencoder-biomed-scib` (and `aspire-biencoder-biomed-scib-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-scib-full`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder-biomed-scib` and `aspire-biencoder-biomed-scib-full` are the single best run among the 3 re-runs. | | TRECCOVID | TRECCOVID | RELISH | RELISH | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| | | MAP | NDCG%20 | MAP | NDCG%20 | | `specter` | 28.24 | 59.28 | 60.62| 77.20 | | `aspire-biencoder-biomed-scib-full`<sup>*</sup> | 30.60 | 62.07 | 61.43| 78.01 | | `aspire-biencoder-biomed-scib` | 30.74 | 60.16 | 61.52| 78.07 | | `aspire-biencoder-biomed-scib-full` | 31.45 | 63.15 | 61.34| 77.89 | **Alternative models:** Besides the above models consider these alternative models also released in the Aspire paper: [`aspire-biencoder-compsci-spec`](https://huggingface.co/allenai/aspire-biencoder-compsci-spec): If you wanted to run on computer science papers. [`aspire-biencoder-biomed-spec`](https://huggingface.co/allenai/aspire-biencoder-biomed-spec): This is an alternative bi-encoder model identical to the above model, except that it is initialized with `allenai/specter` instead of SciBert. This usually under-performs the model released here.

allenai/naqanet

allenai

2022-10-03T21:12:40Z

19

2

allennlp

[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: en tags: - allennlp - question-answering widget: - context: 'A reusable launch system (RLS, or reusable launch vehicle, RLV) is a launch system which is capable of launching a payload into space more than once. This contrasts with expendable launch systems, where each launch vehicle is launched once and then discarded. No completely reusable orbital launch system has ever been created. Two partially reusable launch systems were developed, the Space Shuttle and Falcon 9. The Space Shuttle was partially reusable: the orbiter (which included the Space Shuttle main engines and the Orbital Maneuvering System engines), and the two solid rocket boosters were reused after several months of refitting work for each launch. The external tank was discarded after each flight.' text: How many partially reusable launch systems were developed? example_title: Reusable launch systems - context: Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing. These technologies are used to develop machines that can substitute for humans. Robots can be used in any situation and for any purpose, but today many are used in dangerous environments (including bomb detection and de-activation), manufacturing processes, or where humans cannot survive. Robots can take on any form but some are made to resemble humans in appearance. This is said to help in the acceptance of a robot in certain replicative behaviors usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, and basically anything a human can do. text: What do robots that resemble humans attempt to do? example_title: Robots - context: In the first quarter, the Bears drew first blood as kicker Robbie Gould nailed a 22-yard field goal for the only score of the period. In the second quarter, the Bears increased their lead with Gould nailing a 42-yard field goal. They increased their lead with Cutler firing a 7-yard TD pass to tight end Greg Olsen. The Bears then closed out the first half with Gould's 41-yard field goal. In the third quarter, the Vikes started to rally with running back Adrian Peterson's 1-yard touchdown run (with the extra point attempt blocked). The Bears increased their lead over the Vikings with Cutler's 2-yard TD pass to tight end Desmond Clark. The Vikings then closed out the quarter with quarterback Brett Favre firing a 6-yard TD pass to tight end Visanthe Shiancoe. An exciting fourth quarter ensued. The Vikings started out the quarter's scoring with kicker Ryan Longwell's 41-yard field goal, along with Adrian Peterson's second 1-yard TD run. The Bears then responded with Cutler firing a 20-yard TD pass to wide receiver Earl Bennett. The Vikings then completed the remarkable comeback with Favre finding wide receiver Sidney Rice on a 6-yard TD pass on 4th-and-goal with 15 seconds left in regulation. The Bears then took a knee to force overtime. In overtime, the Bears won the toss and marched down the field, stopping at the 35-yard line. However, the potential game-winning 45-yard field goal attempt by Gould went wide right, giving the Vikings a chance to win. After an exchange of punts, the Vikings had the ball at the 26-yard line with 11 minutes left in the period. On the first play of scrimmage, Favre fired a screen pass to Peterson who caught it and went 16 yards, before being confronted by Hunter Hillenmeyer, who caused Peterson to fumble the ball, which was then recovered by Bears' linebacker Nick Roach. The Bears then won on Jay Cutler's game-winning 39-yard TD pass to wide receiver Devin Aromashodu. With the loss, not only did the Vikings fall to 11-4, they also surrendered homefield advantage to the Saints. text: Who threw the longest touchdown pass of the game? example_title: Argmax - context: Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens. The Texans would respond with fullback Vonta Leach getting a 1-yard touchdown run, yet the Raiders would answer with kicker Sebastian Janikowski getting a 33-yard and a 30-yard field goal. Houston would tie the game in the second quarter with kicker Kris Brown getting a 53-yard and a 24-yard field goal. Oakland would take the lead in the third quarter with wide receiver Johnnie Lee Higgins catching a 29-yard touchdown pass from Russell, followed up by an 80-yard punt return for a touchdown. The Texans tried to rally in the fourth quarter as Brown nailed a 40-yard field goal, yet the Raiders' defense would shut down any possible attempt. text: How many yards was the longest passing touchdown? example_title: Max - context: In 1085, Guadalajara was retaken by the Christian forces of Alfonso VI . The chronicles say that the Christian army was led by Alvar Fanez de Minaya, one of the lieutenants of El Cid. From 1085 until the Battle of Las Navas de Tolosa in 1212, the city suffered wars against the Almoravid and the Almohad Empires. In spite of the wars, the Christian population could definitely settle down in the area thanks to the repopulation with people from the North who received their first fuero in 1133 from Alfonso VII.In 1219, the king Fernando III gave a new fuero to the city .During the reign of Alfonso X of Castile, the protection of the king allowed the city to develop its economy by protecting merchants and allowing markets. text: How many years did the city suffer wars against Almoravid and the Almohad Empires? example_title: Arithmetic --- An augmented version of QANet that adds rudimentary numerical reasoning ability, trained on DROP (Dua et al., 2019), as published in the original DROP paper. An augmented version of QANet model with some rudimentary numerical reasoning abilities. The main idea here is that instead of just predicting a passage span after doing all of the QANet modeling stuff, we add several different ‘answer abilities’: predicting a span from the question, predicting a count, or predicting an arithmetic expression. Near the end of the QANet model, we have a variable that predicts what kind of answer type we need, and each branch has separate modeling logic to predict that answer type. We then marginalize over all possible ways of getting to the right answer through each of these answer types.

allenai/transformer_qa

allenai

2022-10-03T21:12:21Z

12

3

allennlp

[ "allennlp", "tensorboard", "question-answering", "en", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: en tags: - allennlp - question-answering --- A reading comprehension model patterned after the proposed model in Devlin et al, with improvements borrowed from the SQuAD model in the transformers project The model implements a reading comprehension model patterned after the proposed model in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al, 2018), with improvements borrowed from the SQuAD model in the transformers project. It predicts start tokens and end tokens with a linear layer on top of word piece embeddings.

misterkilgore/distilbert-base-uncased-finetuned-disaster-tweet

misterkilgore

2022-10-03T20:09:45Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-30T12:51:56Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-disaster-tweet results: [] ---  # distilbert-base-uncased-finetuned-disaster-tweet This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4052 - Accuracy: 0.8207 - F1: 0.8203 ## 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: 64 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5056 | 1.0 | 96 | 0.4139 | 0.8188 | 0.8179 | | 0.3991 | 2.0 | 192 | 0.4052 | 0.8207 | 0.8203 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

Arklyn/fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1

Arklyn

2022-10-03T20:00:31Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_10_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-03T07:36:55Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_10_0 model-index: - name: fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1 results: [] ---  # fine-tune-Wav2Vec2-XLS-R-300M-Indonesia-1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2114 - Wer: 0.1762 ## 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: 0.0001 - train_batch_size: 36 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9372 | 4.0 | 460 | 2.8359 | 1.0 | | 2.2755 | 8.0 | 920 | 0.3708 | 0.3983 | | 0.6614 | 12.0 | 1380 | 0.2433 | 0.2636 | | 0.5071 | 16.0 | 1840 | 0.2347 | 0.2395 | | 0.4516 | 20.0 | 2300 | 0.2213 | 0.2185 | | 0.4206 | 24.0 | 2760 | 0.2222 | 0.2008 | | 0.3844 | 28.0 | 3220 | 0.2072 | 0.1887 | | 0.3678 | 32.0 | 3680 | 0.2071 | 0.1886 | | 0.3565 | 36.0 | 4140 | 0.2015 | 0.1851 | | 0.3388 | 40.0 | 4600 | 0.2137 | 0.1850 | | 0.3235 | 44.0 | 5060 | 0.2072 | 0.1791 | | 0.3173 | 48.0 | 5520 | 0.2095 | 0.1777 | | 0.3088 | 52.0 | 5980 | 0.2102 | 0.1784 | | 0.3 | 56.0 | 6440 | 0.2164 | 0.1772 | | 0.2957 | 60.0 | 6900 | 0.2114 | 0.1762 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.10.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

neelrr/xlm-roberta-base-finetuned-panx-ta

neelrr

2022-10-03T18:57:36Z

126

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-03T18:52:53Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-ta results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.ta metrics: - name: F1 type: f1 value: 0.8144578313253013 ---  # xlm-roberta-base-finetuned-panx-ta This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - F1: 0.8145 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5477 | 1.0 | 209 | 0.2732 | 0.7305 | | 0.2506 | 2.0 | 418 | 0.2425 | 0.7626 | | 0.168 | 3.0 | 627 | 0.2183 | 0.8145 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

neelrr/xlm-roberta-base-finetuned-panx-hi-mr

neelrr

2022-10-03T18:44:54Z

105

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-03T18:37:10Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-hi-mr results: [] ---  # xlm-roberta-base-finetuned-panx-hi-mr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1942 - F1: 0.8710 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4628 | 1.0 | 417 | 0.2603 | 0.8062 | | 0.2064 | 2.0 | 834 | 0.1951 | 0.8492 | | 0.1289 | 3.0 | 1251 | 0.1942 | 0.8710 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

jinhybr/text-summarization-t5base-xsum

jinhybr

2022-10-03T18:42:32Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-29T13:58:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 28.282 ---  # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4789 - Rouge1: 28.282 - Rouge2: 7.6989 - Rougel: 22.2019 - Rougelsum: 22.197 - Gen Len: 18.8238 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 2.7189 | 1.0 | 12753 | 2.4789 | 28.282 | 7.6989 | 22.2019 | 22.197 | 18.8238 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

albertdestajo/distilbert-base-uncased-finetuned-sst2

albertdestajo

2022-10-03T18:39:23Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-27T20:09:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: train args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9025229357798165 ---  # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2991 - Accuracy: 0.9025 ## 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.227 | 1.0 | 4210 | 0.2991 | 0.9025 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

radhe2205/finetuning-sentiment-model-3000-samples

radhe2205

2022-10-03T18:34:18Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T18:25:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.6566666666666666 - name: F1 type: f1 value: 0.6979472140762463 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.7339 - Accuracy: 0.6567 - F1: 0.6979 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

mofyrt/finetuning-sentiment-model-5000-samples

mofyrt

2022-10-03T18:31:16Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T18:00:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-5000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9104 - name: F1 type: f1 value: 0.9115673114883537 ---  # finetuning-sentiment-model-5000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5802 - Accuracy: 0.9104 - F1: 0.9116 ## 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: 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: 10 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

ad7/finetuning-sentiment-model-3000-samples

ad7

2022-10-03T18:18:09Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T18:08:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.77 - name: F1 type: f1 value: 0.7561837455830389 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6587 - Accuracy: 0.77 - F1: 0.7562 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

eligabel/finetuning-sentiment-model-3000-samples

eligabel

2022-10-03T18:12:07Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T18:02:03Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8166666666666667 - name: F1 type: f1 value: 0.8307692307692307 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6069 - Accuracy: 0.8167 - F1: 0.8308 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

ilex/finetuning-sentiment-model-3000-samples

ilex

2022-10-03T18:10:45Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T18:01:43Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.7533333333333333 - name: F1 type: f1 value: 0.7797619047619048 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6197 - Accuracy: 0.7533 - F1: 0.7798 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

chenyueg/finetuning-sentiment-model-3000-samples

chenyueg

2022-10-03T18:02:20Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T04:48:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.6566666666666666 - name: F1 type: f1 value: 0.6555183946488293 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6751 - Accuracy: 0.6567 - F1: 0.6555 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

Bakuraza/FirstLunarLanding

Bakuraza

2022-10-03T17:16:52Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-03T17:16:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 229.59 +/- 12.98 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

model-attribution-challenge/codegen-350M-multi

model-attribution-challenge

2022-10-03T16:18:49Z

108

2

transformers

[ "transformers", "pytorch", "codegen", "text-generation", "arxiv:2203.13474", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-07-26T13:36:04Z

--- license: bsd-3-clause --- # CodeGen (CodeGen-Multi 350M) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Multi 350M** in the paper, where "Multi" means the model is initialized with *CodeGen-NL 350M* and further pre-trained on a dataset of multiple programming languages, and "350M" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Multi 350M) was firstly initialized with *CodeGen-NL 350M*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

jschmock/gewerke

jschmock

2022-10-03T15:49:04Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-27T10:30:34Z

--- tags: - generated_from_trainer metrics: - f1 model-index: - name: gewerke results: [] ---  # gewerke This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0089 - F1: 0.9974 ## Label-Übersetzung - 0 Abwasser-Wasser-Gasanlagen - 1 Andere Anlagen - 2 Gebäudeautomation - 3 Kälteanlagen - 4 Lufttechnische Anlagen - 5 Starkstromanlagen - 6 Wärmeversorungsanlagen ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 90 | 0.0444 | 0.9886 | | No log | 1.99 | 180 | 0.0182 | 0.9947 | | No log | 2.99 | 270 | 0.0103 | 0.9974 | | No log | 3.99 | 360 | 0.0152 | 0.9946 | | No log | 4.99 | 450 | 0.0089 | 0.9974 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/jang-sung-rak-style

sd-concepts-library

2022-10-03T15:43:16Z

0

1

null

[ "license:mit", "region:us" ]

null

2022-10-03T15:43:05Z

--- license: mit --- ### Jang-Sung-Rak-Style on Stable Diffusion This is the `<Jang-Sung-Rak-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<Jang-Sung-Rak-style> 0](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/1.jpeg) ![<Jang-Sung-Rak-style> 1](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/4.jpeg) ![<Jang-Sung-Rak-style> 2](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/2.jpeg) ![<Jang-Sung-Rak-style> 3](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/0.jpeg) ![<Jang-Sung-Rak-style> 4](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/3.jpeg) ![<Jang-Sung-Rak-style> 5](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/6.jpeg) ![<Jang-Sung-Rak-style> 6](https://huggingface.co/sd-concepts-library/jang-sung-rak-style/resolve/main/concept_images/5.jpeg)

GItaf/gpt2-gpt2-mc-weight2-epoch15

GItaf

2022-10-03T14:40:03Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-03T06:53:43Z

--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-mc-weight2-epoch15 results: [] ---  # gpt2-gpt2-mc-weight2-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.3067 - Cls loss: 3.6680 - Lm loss: 3.9663 - Cls Accuracy: 0.6069 - Cls F1: 0.6023 - Cls Precision: 0.6050 - Cls Recall: 0.6069 - Perplexity: 52.79 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:| | 8.1971 | 1.0 | 3470 | 7.6018 | 1.7777 | 4.0471 | 0.5251 | 0.4892 | 0.5282 | 0.5251 | 57.23 | | 7.0508 | 2.0 | 6940 | 7.2679 | 1.6195 | 4.0269 | 0.6058 | 0.6001 | 0.6226 | 0.6058 | 56.08 | | 6.5979 | 3.0 | 10410 | 7.4637 | 1.7253 | 4.0112 | 0.6184 | 0.6109 | 0.6367 | 0.6184 | 55.21 | | 6.1811 | 4.0 | 13880 | 7.8253 | 1.9084 | 4.0063 | 0.6121 | 0.6074 | 0.6207 | 0.6121 | 54.94 | | 5.7242 | 5.0 | 17350 | 8.1576 | 2.0824 | 3.9903 | 0.6219 | 0.6166 | 0.6195 | 0.6219 | 54.07 | | 5.2588 | 6.0 | 20820 | 8.9472 | 2.4785 | 3.9872 | 0.6006 | 0.5942 | 0.6169 | 0.6006 | 53.90 | | 4.8854 | 7.0 | 24290 | 9.2399 | 2.6264 | 3.9840 | 0.6063 | 0.5988 | 0.6123 | 0.6063 | 53.73 | | 4.5785 | 8.0 | 27760 | 9.7123 | 2.8660 | 3.9768 | 0.6121 | 0.6076 | 0.6124 | 0.6121 | 53.35 | | 4.3508 | 9.0 | 31230 | 10.2550 | 3.1400 | 3.9712 | 0.6086 | 0.6017 | 0.6121 | 0.6086 | 53.05 | | 4.1817 | 10.0 | 34700 | 10.3110 | 3.1681 | 3.9711 | 0.6058 | 0.5990 | 0.6047 | 0.6058 | 53.04 | | 4.0546 | 11.0 | 38170 | 11.0526 | 3.5396 | 3.9693 | 0.5988 | 0.5929 | 0.5997 | 0.5988 | 52.95 | | 3.9481 | 12.0 | 41640 | 11.0193 | 3.5238 | 3.9675 | 0.6086 | 0.6038 | 0.6049 | 0.6086 | 52.85 | | 3.9008 | 13.0 | 45110 | 11.2499 | 3.6394 | 3.9669 | 0.6121 | 0.6073 | 0.6090 | 0.6121 | 52.82 | | 3.8558 | 14.0 | 48580 | 11.3606 | 3.6948 | 3.9666 | 0.6063 | 0.6000 | 0.6034 | 0.6063 | 52.81 | | 3.8297 | 15.0 | 52050 | 11.3067 | 3.6680 | 3.9663 | 0.6069 | 0.6023 | 0.6050 | 0.6069 | 52.79 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

ironbar/stable_diffusion_gmartinez

ironbar

2022-10-03T13:50:16Z

0

null

[ "region:us" ]

null

2022-10-03T13:29:06Z

Stable-diffusion fine-tuned with this [repo](https://github.com/JoePenna/Dreambooth-Stable-Diffusion/) to create images from my friend Gonzalo. ![joker](https://huggingface.co/ironbar/stable_diffusion_gmartinez/blob/main/joker.jpeg)

zannabethl/opus-mt-en-ro-finetuned-en-to-ro

zannabethl

2022-10-03T13:00:35Z

116

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-03T12:22:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: [] ---  # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 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: 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: 1 ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

damilojohn/Bert2BertForTextDescrambling

damilojohn

2022-10-03T12:40:40Z

111

1

transformers

[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-03T00:44:12Z

--- license: apache-2.0 --- This model receives scrambled or incoherent sentences as input and returns a meaningful sentence using the same words in the input . A form of grammar correction if you may . It was trained on a dataset of permutated sentences derived from wikipedia pages as input with the correct arrangement of words as labels . It is an encoder-decoder model that uses BERT's weight in both it's encoder and decoder .

cw1521/opus-mt-st-en

cw1521

2022-10-03T12:36:01Z

115

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-29T01:37:55Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: opus-mt-st-en results: [] ---  # opus-mt-st-en This model is a fine-tuned version of [cw1521/opus-mt-st-en](https://huggingface.co/cw1521/opus-mt-st-en) on an unknown 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: 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: 1 ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

cw1521/opus-mt-en-st

cw1521

2022-10-03T12:31:35Z

112

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-29T02:29:39Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-st results: [] ---  # opus-mt-en-st This model is a fine-tuned version of [cw1521/opus-mt-en-st](https://huggingface.co/cw1521/opus-mt-en-st) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 - Bleu: 77.1725 - Gen Len: 60.3696 ## 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.3768 | 1.0 | 969 | 0.3710 | 77.1725 | 60.3696 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/aadhav-face

sd-concepts-library

2022-10-03T12:22:50Z

0

null

[ "license:mit", "region:us" ]

null

2022-10-03T12:22:47Z

--- license: mit --- ### aadhav face on Stable Diffusion This is the `<aadhav-face>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<aadhav-face> 0](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/1.jpeg) ![<aadhav-face> 1](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/2.jpeg) ![<aadhav-face> 2](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/0.jpeg) ![<aadhav-face> 3](https://huggingface.co/sd-concepts-library/aadhav-face/resolve/main/concept_images/3.jpeg)

Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit

Muennighoff

2022-10-03T12:16:09Z

828

6

sentence-transformers

[ "sentence-transformers", "pytorch", "gptj", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-03-02T23:29:04Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-5.8B-weightedmean-nli-bitfit results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 74.07462686567165 - type: ap value: 37.44692407529112 - type: f1 value: 68.28971003916419 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 66.63811563169165 - type: ap value: 78.57252079915924 - type: f1 value: 64.5543087846584 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 77.21889055472263 - type: ap value: 25.663426367826712 - type: f1 value: 64.26265688503176 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 58.06209850107067 - type: ap value: 14.028219107023915 - type: f1 value: 48.10387189660778 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 82.30920000000002 - type: ap value: 76.88786578621213 - type: f1 value: 82.15455656065011 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 41.584 - type: f1 value: 41.203137944390114 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.288000000000004 - type: f1 value: 34.672995558518096 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 38.34 - type: f1 value: 37.608755629529455 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 37.839999999999996 - type: f1 value: 36.86898201563507 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 30.936000000000003 - type: f1 value: 30.49401738527071 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 33.75 - type: f1 value: 33.38338946025617 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 13.727 - type: map_at_10 value: 26.740000000000002 - type: map_at_100 value: 28.218 - type: map_at_1000 value: 28.246 - type: map_at_3 value: 21.728 - type: map_at_5 value: 24.371000000000002 - type: ndcg_at_1 value: 13.727 - type: ndcg_at_10 value: 35.07 - type: ndcg_at_100 value: 41.947 - type: ndcg_at_1000 value: 42.649 - type: ndcg_at_3 value: 24.484 - type: ndcg_at_5 value: 29.282999999999998 - type: precision_at_1 value: 13.727 - type: precision_at_10 value: 6.223 - type: precision_at_100 value: 0.9369999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 10.835 - type: precision_at_5 value: 8.848 - type: recall_at_1 value: 13.727 - type: recall_at_10 value: 62.233000000000004 - type: recall_at_100 value: 93.67 - type: recall_at_1000 value: 99.14699999999999 - type: recall_at_3 value: 32.504 - type: recall_at_5 value: 44.239 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 40.553923271901695 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 32.49323183712211 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 55.89811361443445 - type: mrr value: 70.16235764850724 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 82.50506557805856 - type: cos_sim_spearman value: 79.50000423261176 - type: euclidean_pearson value: 75.76190885392926 - type: euclidean_spearman value: 76.7330737163434 - type: manhattan_pearson value: 75.825318036112 - type: manhattan_spearman value: 76.7415076434559 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 75.49060542797494 - type: f1 value: 75.15379262352123 - type: precision value: 74.99391092553932 - type: recall value: 75.49060542797494 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.4182258419546555 - type: f1 value: 0.4182258419546555 - type: precision value: 0.4182258419546555 - type: recall value: 0.4182258419546555 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.013855213023900243 - type: f1 value: 0.0115460108532502 - type: precision value: 0.010391409767925183 - type: recall value: 0.013855213023900243 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.315955766192733 - type: f1 value: 0.315955766192733 - type: precision value: 0.315955766192733 - type: recall value: 0.315955766192733 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 81.74025974025973 - type: f1 value: 81.66568824876 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 33.59451202614059 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 29.128241446157165 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 26.715 - type: map_at_10 value: 35.007 - type: map_at_100 value: 36.352000000000004 - type: map_at_1000 value: 36.51 - type: map_at_3 value: 32.257999999999996 - type: map_at_5 value: 33.595000000000006 - type: ndcg_at_1 value: 33.906 - type: ndcg_at_10 value: 40.353 - type: ndcg_at_100 value: 45.562999999999995 - type: ndcg_at_1000 value: 48.454 - type: ndcg_at_3 value: 36.349 - type: ndcg_at_5 value: 37.856 - type: precision_at_1 value: 33.906 - type: precision_at_10 value: 7.854 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 17.549 - type: precision_at_5 value: 12.561 - type: recall_at_1 value: 26.715 - type: recall_at_10 value: 49.508 - type: recall_at_100 value: 71.76599999999999 - type: recall_at_1000 value: 91.118 - type: recall_at_3 value: 37.356 - type: recall_at_5 value: 41.836 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 19.663 - type: map_at_10 value: 27.086 - type: map_at_100 value: 28.066999999999997 - type: map_at_1000 value: 28.18 - type: map_at_3 value: 24.819 - type: map_at_5 value: 26.332 - type: ndcg_at_1 value: 25.732 - type: ndcg_at_10 value: 31.613999999999997 - type: ndcg_at_100 value: 35.757 - type: ndcg_at_1000 value: 38.21 - type: ndcg_at_3 value: 28.332 - type: ndcg_at_5 value: 30.264000000000003 - type: precision_at_1 value: 25.732 - type: precision_at_10 value: 6.038 - type: precision_at_100 value: 1.034 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 13.864 - type: precision_at_5 value: 10.241999999999999 - type: recall_at_1 value: 19.663 - type: recall_at_10 value: 39.585 - type: recall_at_100 value: 57.718 - type: recall_at_1000 value: 74.26700000000001 - type: recall_at_3 value: 29.845 - type: recall_at_5 value: 35.105 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.125 - type: map_at_10 value: 39.824 - type: map_at_100 value: 40.935 - type: map_at_1000 value: 41.019 - type: map_at_3 value: 37.144 - type: map_at_5 value: 38.647999999999996 - type: ndcg_at_1 value: 34.922 - type: ndcg_at_10 value: 45.072 - type: ndcg_at_100 value: 50.046 - type: ndcg_at_1000 value: 51.895 - type: ndcg_at_3 value: 40.251 - type: ndcg_at_5 value: 42.581 - type: precision_at_1 value: 34.922 - type: precision_at_10 value: 7.303999999999999 - type: precision_at_100 value: 1.0739999999999998 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 17.994 - type: precision_at_5 value: 12.475999999999999 - type: recall_at_1 value: 30.125 - type: recall_at_10 value: 57.253 - type: recall_at_100 value: 79.35799999999999 - type: recall_at_1000 value: 92.523 - type: recall_at_3 value: 44.088 - type: recall_at_5 value: 49.893 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.298000000000002 - type: map_at_10 value: 21.479 - type: map_at_100 value: 22.387 - type: map_at_1000 value: 22.483 - type: map_at_3 value: 19.743 - type: map_at_5 value: 20.444000000000003 - type: ndcg_at_1 value: 17.740000000000002 - type: ndcg_at_10 value: 24.887 - type: ndcg_at_100 value: 29.544999999999998 - type: ndcg_at_1000 value: 32.417 - type: ndcg_at_3 value: 21.274 - type: ndcg_at_5 value: 22.399 - type: precision_at_1 value: 17.740000000000002 - type: precision_at_10 value: 3.932 - type: precision_at_100 value: 0.666 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 8.927 - type: precision_at_5 value: 6.056 - type: recall_at_1 value: 16.298000000000002 - type: recall_at_10 value: 34.031 - type: recall_at_100 value: 55.769000000000005 - type: recall_at_1000 value: 78.19500000000001 - type: recall_at_3 value: 23.799999999999997 - type: recall_at_5 value: 26.562 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 10.958 - type: map_at_10 value: 16.999 - type: map_at_100 value: 17.979 - type: map_at_1000 value: 18.112000000000002 - type: map_at_3 value: 15.010000000000002 - type: map_at_5 value: 16.256999999999998 - type: ndcg_at_1 value: 14.179 - type: ndcg_at_10 value: 20.985 - type: ndcg_at_100 value: 26.216 - type: ndcg_at_1000 value: 29.675 - type: ndcg_at_3 value: 17.28 - type: ndcg_at_5 value: 19.301 - type: precision_at_1 value: 14.179 - type: precision_at_10 value: 3.968 - type: precision_at_100 value: 0.784 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 8.541 - type: precision_at_5 value: 6.468 - type: recall_at_1 value: 10.958 - type: recall_at_10 value: 29.903000000000002 - type: recall_at_100 value: 53.413 - type: recall_at_1000 value: 78.74799999999999 - type: recall_at_3 value: 19.717000000000002 - type: recall_at_5 value: 24.817 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 21.217 - type: map_at_10 value: 29.677 - type: map_at_100 value: 30.928 - type: map_at_1000 value: 31.063000000000002 - type: map_at_3 value: 26.611 - type: map_at_5 value: 28.463 - type: ndcg_at_1 value: 26.083000000000002 - type: ndcg_at_10 value: 35.217 - type: ndcg_at_100 value: 40.715 - type: ndcg_at_1000 value: 43.559 - type: ndcg_at_3 value: 30.080000000000002 - type: ndcg_at_5 value: 32.701 - type: precision_at_1 value: 26.083000000000002 - type: precision_at_10 value: 6.622 - type: precision_at_100 value: 1.115 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 14.629 - type: precision_at_5 value: 10.837 - type: recall_at_1 value: 21.217 - type: recall_at_10 value: 47.031 - type: recall_at_100 value: 70.378 - type: recall_at_1000 value: 89.704 - type: recall_at_3 value: 32.427 - type: recall_at_5 value: 39.31 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 19.274 - type: map_at_10 value: 26.398 - type: map_at_100 value: 27.711000000000002 - type: map_at_1000 value: 27.833000000000002 - type: map_at_3 value: 24.294 - type: map_at_5 value: 25.385 - type: ndcg_at_1 value: 24.886 - type: ndcg_at_10 value: 30.909 - type: ndcg_at_100 value: 36.941 - type: ndcg_at_1000 value: 39.838 - type: ndcg_at_3 value: 27.455000000000002 - type: ndcg_at_5 value: 28.828 - type: precision_at_1 value: 24.886 - type: precision_at_10 value: 5.6739999999999995 - type: precision_at_100 value: 1.0290000000000001 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 13.242 - type: precision_at_5 value: 9.292 - type: recall_at_1 value: 19.274 - type: recall_at_10 value: 39.643 - type: recall_at_100 value: 66.091 - type: recall_at_1000 value: 86.547 - type: recall_at_3 value: 29.602 - type: recall_at_5 value: 33.561 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.653666666666666 - type: map_at_10 value: 25.606666666666666 - type: map_at_100 value: 26.669333333333334 - type: map_at_1000 value: 26.795833333333334 - type: map_at_3 value: 23.43433333333333 - type: map_at_5 value: 24.609666666666666 - type: ndcg_at_1 value: 22.742083333333333 - type: ndcg_at_10 value: 29.978333333333335 - type: ndcg_at_100 value: 34.89808333333333 - type: ndcg_at_1000 value: 37.806583333333336 - type: ndcg_at_3 value: 26.223666666666674 - type: ndcg_at_5 value: 27.91033333333333 - type: precision_at_1 value: 22.742083333333333 - type: precision_at_10 value: 5.397083333333334 - type: precision_at_100 value: 0.9340000000000002 - type: precision_at_1000 value: 0.13691666666666663 - type: precision_at_3 value: 12.331083333333332 - type: precision_at_5 value: 8.805499999999999 - type: recall_at_1 value: 18.653666666666666 - type: recall_at_10 value: 39.22625000000001 - type: recall_at_100 value: 61.31049999999999 - type: recall_at_1000 value: 82.19058333333334 - type: recall_at_3 value: 28.517333333333333 - type: recall_at_5 value: 32.9565 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 16.07 - type: map_at_10 value: 21.509 - type: map_at_100 value: 22.335 - type: map_at_1000 value: 22.437 - type: map_at_3 value: 19.717000000000002 - type: map_at_5 value: 20.574 - type: ndcg_at_1 value: 18.865000000000002 - type: ndcg_at_10 value: 25.135999999999996 - type: ndcg_at_100 value: 29.483999999999998 - type: ndcg_at_1000 value: 32.303 - type: ndcg_at_3 value: 21.719 - type: ndcg_at_5 value: 23.039 - type: precision_at_1 value: 18.865000000000002 - type: precision_at_10 value: 4.263999999999999 - type: precision_at_100 value: 0.696 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 9.866999999999999 - type: precision_at_5 value: 6.902 - type: recall_at_1 value: 16.07 - type: recall_at_10 value: 33.661 - type: recall_at_100 value: 54.001999999999995 - type: recall_at_1000 value: 75.564 - type: recall_at_3 value: 23.956 - type: recall_at_5 value: 27.264 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 10.847 - type: map_at_10 value: 15.518 - type: map_at_100 value: 16.384 - type: map_at_1000 value: 16.506 - type: map_at_3 value: 14.093 - type: map_at_5 value: 14.868 - type: ndcg_at_1 value: 13.764999999999999 - type: ndcg_at_10 value: 18.766 - type: ndcg_at_100 value: 23.076 - type: ndcg_at_1000 value: 26.344 - type: ndcg_at_3 value: 16.150000000000002 - type: ndcg_at_5 value: 17.373 - type: precision_at_1 value: 13.764999999999999 - type: precision_at_10 value: 3.572 - type: precision_at_100 value: 0.6779999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 7.88 - type: precision_at_5 value: 5.712 - type: recall_at_1 value: 10.847 - type: recall_at_10 value: 25.141999999999996 - type: recall_at_100 value: 44.847 - type: recall_at_1000 value: 68.92099999999999 - type: recall_at_3 value: 17.721999999999998 - type: recall_at_5 value: 20.968999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.377 - type: map_at_10 value: 26.005 - type: map_at_100 value: 26.996 - type: map_at_1000 value: 27.116 - type: map_at_3 value: 23.712 - type: map_at_5 value: 24.859 - type: ndcg_at_1 value: 22.201 - type: ndcg_at_10 value: 30.635 - type: ndcg_at_100 value: 35.623 - type: ndcg_at_1000 value: 38.551 - type: ndcg_at_3 value: 26.565 - type: ndcg_at_5 value: 28.28 - type: precision_at_1 value: 22.201 - type: precision_at_10 value: 5.41 - type: precision_at_100 value: 0.88 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 12.531 - type: precision_at_5 value: 8.806 - type: recall_at_1 value: 18.377 - type: recall_at_10 value: 40.908 - type: recall_at_100 value: 63.563 - type: recall_at_1000 value: 84.503 - type: recall_at_3 value: 29.793999999999997 - type: recall_at_5 value: 34.144999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 20.246 - type: map_at_10 value: 27.528000000000002 - type: map_at_100 value: 28.78 - type: map_at_1000 value: 29.002 - type: map_at_3 value: 25.226 - type: map_at_5 value: 26.355 - type: ndcg_at_1 value: 25.099 - type: ndcg_at_10 value: 32.421 - type: ndcg_at_100 value: 37.2 - type: ndcg_at_1000 value: 40.693 - type: ndcg_at_3 value: 28.768 - type: ndcg_at_5 value: 30.23 - type: precision_at_1 value: 25.099 - type: precision_at_10 value: 6.245 - type: precision_at_100 value: 1.269 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 13.767999999999999 - type: precision_at_5 value: 9.881 - type: recall_at_1 value: 20.246 - type: recall_at_10 value: 41.336 - type: recall_at_100 value: 63.098 - type: recall_at_1000 value: 86.473 - type: recall_at_3 value: 30.069000000000003 - type: recall_at_5 value: 34.262 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 14.054 - type: map_at_10 value: 20.25 - type: map_at_100 value: 21.178 - type: map_at_1000 value: 21.288999999999998 - type: map_at_3 value: 18.584999999999997 - type: map_at_5 value: 19.536 - type: ndcg_at_1 value: 15.527 - type: ndcg_at_10 value: 23.745 - type: ndcg_at_100 value: 28.610999999999997 - type: ndcg_at_1000 value: 31.740000000000002 - type: ndcg_at_3 value: 20.461 - type: ndcg_at_5 value: 22.072 - type: precision_at_1 value: 15.527 - type: precision_at_10 value: 3.882 - type: precision_at_100 value: 0.6930000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 9.181000000000001 - type: precision_at_5 value: 6.433 - type: recall_at_1 value: 14.054 - type: recall_at_10 value: 32.714 - type: recall_at_100 value: 55.723 - type: recall_at_1000 value: 79.72399999999999 - type: recall_at_3 value: 23.832 - type: recall_at_5 value: 27.754 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 6.122 - type: map_at_10 value: 11.556 - type: map_at_100 value: 12.998000000000001 - type: map_at_1000 value: 13.202 - type: map_at_3 value: 9.657 - type: map_at_5 value: 10.585 - type: ndcg_at_1 value: 15.049000000000001 - type: ndcg_at_10 value: 17.574 - type: ndcg_at_100 value: 24.465999999999998 - type: ndcg_at_1000 value: 28.511999999999997 - type: ndcg_at_3 value: 13.931 - type: ndcg_at_5 value: 15.112 - type: precision_at_1 value: 15.049000000000001 - type: precision_at_10 value: 5.831 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 10.749 - type: precision_at_5 value: 8.365 - type: recall_at_1 value: 6.122 - type: recall_at_10 value: 22.207 - type: recall_at_100 value: 47.08 - type: recall_at_1000 value: 70.182 - type: recall_at_3 value: 13.416 - type: recall_at_5 value: 16.672 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: f097057d03ed98220bc7309ddb10b71a54d667d6 metrics: - type: map_at_1 value: 4.672 - type: map_at_10 value: 10.534 - type: map_at_100 value: 14.798 - type: map_at_1000 value: 15.927 - type: map_at_3 value: 7.317 - type: map_at_5 value: 8.726 - type: ndcg_at_1 value: 36.5 - type: ndcg_at_10 value: 26.098 - type: ndcg_at_100 value: 29.215999999999998 - type: ndcg_at_1000 value: 36.254999999999995 - type: ndcg_at_3 value: 29.247 - type: ndcg_at_5 value: 27.692 - type: precision_at_1 value: 47.25 - type: precision_at_10 value: 22.625 - type: precision_at_100 value: 7.042 - type: precision_at_1000 value: 1.6129999999999998 - type: precision_at_3 value: 34.083000000000006 - type: precision_at_5 value: 29.5 - type: recall_at_1 value: 4.672 - type: recall_at_10 value: 15.638 - type: recall_at_100 value: 36.228 - type: recall_at_1000 value: 58.831 - type: recall_at_3 value: 8.578 - type: recall_at_5 value: 11.18 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 829147f8f75a25f005913200eb5ed41fae320aa1 metrics: - type: accuracy value: 49.919999999999995 - type: f1 value: 45.37973678791632 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: 1429cf27e393599b8b359b9b72c666f96b2525f9 metrics: - type: map_at_1 value: 25.801000000000002 - type: map_at_10 value: 33.941 - type: map_at_100 value: 34.73 - type: map_at_1000 value: 34.793 - type: map_at_3 value: 31.705 - type: map_at_5 value: 33.047 - type: ndcg_at_1 value: 27.933000000000003 - type: ndcg_at_10 value: 38.644 - type: ndcg_at_100 value: 42.594 - type: ndcg_at_1000 value: 44.352000000000004 - type: ndcg_at_3 value: 34.199 - type: ndcg_at_5 value: 36.573 - type: precision_at_1 value: 27.933000000000003 - type: precision_at_10 value: 5.603000000000001 - type: precision_at_100 value: 0.773 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 14.171 - type: precision_at_5 value: 9.786999999999999 - type: recall_at_1 value: 25.801000000000002 - type: recall_at_10 value: 50.876 - type: recall_at_100 value: 69.253 - type: recall_at_1000 value: 82.907 - type: recall_at_3 value: 38.879000000000005 - type: recall_at_5 value: 44.651999999999994 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be metrics: - type: map_at_1 value: 9.142 - type: map_at_10 value: 13.841999999999999 - type: map_at_100 value: 14.960999999999999 - type: map_at_1000 value: 15.187000000000001 - type: map_at_3 value: 11.966000000000001 - type: map_at_5 value: 12.921 - type: ndcg_at_1 value: 18.364 - type: ndcg_at_10 value: 18.590999999999998 - type: ndcg_at_100 value: 24.153 - type: ndcg_at_1000 value: 29.104000000000003 - type: ndcg_at_3 value: 16.323 - type: ndcg_at_5 value: 17.000999999999998 - type: precision_at_1 value: 18.364 - type: precision_at_10 value: 5.216 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 10.751 - type: precision_at_5 value: 7.932 - type: recall_at_1 value: 9.142 - type: recall_at_10 value: 22.747 - type: recall_at_100 value: 44.585 - type: recall_at_1000 value: 75.481 - type: recall_at_3 value: 14.602 - type: recall_at_5 value: 17.957 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: 766870b35a1b9ca65e67a0d1913899973551fc6c metrics: - type: map_at_1 value: 18.677 - type: map_at_10 value: 26.616 - type: map_at_100 value: 27.605 - type: map_at_1000 value: 27.711999999999996 - type: map_at_3 value: 24.396 - type: map_at_5 value: 25.627 - type: ndcg_at_1 value: 37.352999999999994 - type: ndcg_at_10 value: 33.995 - type: ndcg_at_100 value: 38.423 - type: ndcg_at_1000 value: 40.947 - type: ndcg_at_3 value: 29.885 - type: ndcg_at_5 value: 31.874999999999996 - type: precision_at_1 value: 37.352999999999994 - type: precision_at_10 value: 7.539999999999999 - type: precision_at_100 value: 1.107 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.938 - type: precision_at_5 value: 12.943 - type: recall_at_1 value: 18.677 - type: recall_at_10 value: 37.698 - type: recall_at_100 value: 55.354000000000006 - type: recall_at_1000 value: 72.255 - type: recall_at_3 value: 28.406 - type: recall_at_5 value: 32.357 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4 metrics: - type: accuracy value: 74.3292 - type: ap value: 68.30186110189658 - type: f1 value: 74.20709636944783 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: validation revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849 metrics: - type: map_at_1 value: 6.889000000000001 - type: map_at_10 value: 12.321 - type: map_at_100 value: 13.416 - type: map_at_1000 value: 13.525 - type: map_at_3 value: 10.205 - type: map_at_5 value: 11.342 - type: ndcg_at_1 value: 7.092 - type: ndcg_at_10 value: 15.827 - type: ndcg_at_100 value: 21.72 - type: ndcg_at_1000 value: 24.836 - type: ndcg_at_3 value: 11.393 - type: ndcg_at_5 value: 13.462 - type: precision_at_1 value: 7.092 - type: precision_at_10 value: 2.7969999999999997 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 5.019 - type: precision_at_5 value: 4.06 - type: recall_at_1 value: 6.889000000000001 - type: recall_at_10 value: 26.791999999999998 - type: recall_at_100 value: 55.371 - type: recall_at_1000 value: 80.12899999999999 - type: recall_at_3 value: 14.573 - type: recall_at_5 value: 19.557 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 89.6374829001368 - type: f1 value: 89.20878379358307 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 84.54212454212454 - type: f1 value: 82.81080100037023 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 86.46430953969313 - type: f1 value: 86.00019824223267 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 81.31850923896022 - type: f1 value: 81.07860454762863 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 58.23234134098243 - type: f1 value: 56.63845098081841 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: a7e2a951126a26fc8c6a69f835f33a346ba259e3 metrics: - type: accuracy value: 72.28571428571429 - type: f1 value: 70.95796714592039 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 70.68171454628363 - type: f1 value: 52.57188062729139 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 60.521273598196665 - type: f1 value: 42.70492970339204 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 64.32288192128087 - type: f1 value: 45.97360620220273 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 58.67209520826808 - type: f1 value: 42.82844991304579 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) config: hi split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 41.95769092864826 - type: f1 value: 28.914127631431263 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) config: th split: test revision: 6299947a7777084cc2d4b64235bf7190381ce755 metrics: - type: accuracy value: 55.28390596745027 - type: f1 value: 38.33899250561289 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 072a486a144adf7f4479a4a0dddb2152e161e1ea metrics: - type: accuracy value: 70.00336247478144 - type: f1 value: 68.72041942191649 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.0268997982515 - type: f1 value: 75.29844481506652 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: dcefc037ef84348e49b0d29109e891c01067226b metrics: - type: v_measure value: 30.327566856300813 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 3cd0e71dfbe09d4de0f9e5ecba43e7ce280959dc metrics: - type: v_measure value: 28.01650210863619 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.11041256752524 - type: mrr value: 32.14172939750204 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: 7eb63cc0c1eb59324d709ebed25fcab851fa7610 metrics: - type: map_at_1 value: 3.527 - type: map_at_10 value: 9.283 - type: map_at_100 value: 11.995000000000001 - type: map_at_1000 value: 13.33 - type: map_at_3 value: 6.223 - type: map_at_5 value: 7.68 - type: ndcg_at_1 value: 36.223 - type: ndcg_at_10 value: 28.255999999999997 - type: ndcg_at_100 value: 26.355 - type: ndcg_at_1000 value: 35.536 - type: ndcg_at_3 value: 31.962000000000003 - type: ndcg_at_5 value: 30.61 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 21.889 - type: precision_at_100 value: 7.1080000000000005 - type: precision_at_1000 value: 1.989 - type: precision_at_3 value: 30.857 - type: precision_at_5 value: 27.307 - type: recall_at_1 value: 3.527 - type: recall_at_10 value: 14.015 - type: recall_at_100 value: 28.402 - type: recall_at_1000 value: 59.795 - type: recall_at_3 value: 7.5969999999999995 - type: recall_at_5 value: 10.641 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: 6062aefc120bfe8ece5897809fb2e53bfe0d128c metrics: - type: map_at_1 value: 11.631 - type: map_at_10 value: 19.532 - type: map_at_100 value: 20.821 - type: map_at_1000 value: 20.910999999999998 - type: map_at_3 value: 16.597 - type: map_at_5 value: 18.197 - type: ndcg_at_1 value: 13.413 - type: ndcg_at_10 value: 24.628 - type: ndcg_at_100 value: 30.883 - type: ndcg_at_1000 value: 33.216 - type: ndcg_at_3 value: 18.697 - type: ndcg_at_5 value: 21.501 - type: precision_at_1 value: 13.413 - type: precision_at_10 value: 4.571 - type: precision_at_100 value: 0.812 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.845 - type: precision_at_5 value: 6.889000000000001 - type: recall_at_1 value: 11.631 - type: recall_at_10 value: 38.429 - type: recall_at_100 value: 67.009 - type: recall_at_1000 value: 84.796 - type: recall_at_3 value: 22.74 - type: recall_at_5 value: 29.266 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: 6205996560df11e3a3da9ab4f926788fc30a7db4 metrics: - type: map_at_1 value: 66.64 - type: map_at_10 value: 80.394 - type: map_at_100 value: 81.099 - type: map_at_1000 value: 81.122 - type: map_at_3 value: 77.289 - type: map_at_5 value: 79.25999999999999 - type: ndcg_at_1 value: 76.85 - type: ndcg_at_10 value: 84.68 - type: ndcg_at_100 value: 86.311 - type: ndcg_at_1000 value: 86.49900000000001 - type: ndcg_at_3 value: 81.295 - type: ndcg_at_5 value: 83.199 - type: precision_at_1 value: 76.85 - type: precision_at_10 value: 12.928999999999998 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.557 - type: precision_at_5 value: 23.576 - type: recall_at_1 value: 66.64 - type: recall_at_10 value: 93.059 - type: recall_at_100 value: 98.922 - type: recall_at_1000 value: 99.883 - type: recall_at_3 value: 83.49499999999999 - type: recall_at_5 value: 88.729 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: b2805658ae38990172679479369a78b86de8c390 metrics: - type: v_measure value: 42.17131361041068 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 48.01815621479994 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: 5c59ef3e437a0a9651c8fe6fde943e7dce59fba5 metrics: - type: map_at_1 value: 3.198 - type: map_at_10 value: 7.550999999999999 - type: map_at_100 value: 9.232 - type: map_at_1000 value: 9.51 - type: map_at_3 value: 5.2940000000000005 - type: map_at_5 value: 6.343999999999999 - type: ndcg_at_1 value: 15.8 - type: ndcg_at_10 value: 13.553999999999998 - type: ndcg_at_100 value: 20.776 - type: ndcg_at_1000 value: 26.204 - type: ndcg_at_3 value: 12.306000000000001 - type: ndcg_at_5 value: 10.952 - type: precision_at_1 value: 15.8 - type: precision_at_10 value: 7.180000000000001 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.307 - type: precision_at_3 value: 11.333 - type: precision_at_5 value: 9.62 - type: recall_at_1 value: 3.198 - type: recall_at_10 value: 14.575 - type: recall_at_100 value: 35.758 - type: recall_at_1000 value: 62.317 - type: recall_at_3 value: 6.922000000000001 - type: recall_at_5 value: 9.767000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cos_sim_pearson value: 84.5217161312271 - type: cos_sim_spearman value: 79.58562467776268 - type: euclidean_pearson value: 76.69364353942403 - type: euclidean_spearman value: 74.68959282070473 - type: manhattan_pearson value: 76.81159265133732 - type: manhattan_spearman value: 74.7519444048176 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: fdf84275bb8ce4b49c971d02e84dd1abc677a50f metrics: - type: cos_sim_pearson value: 83.70403706922605 - type: cos_sim_spearman value: 74.28502198729447 - type: euclidean_pearson value: 83.32719404608066 - type: euclidean_spearman value: 75.92189433460788 - type: manhattan_pearson value: 83.35841543005293 - type: manhattan_spearman value: 75.94458615451978 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 1591bfcbe8c69d4bf7fe2a16e2451017832cafb9 metrics: - type: cos_sim_pearson value: 84.94127878986795 - type: cos_sim_spearman value: 85.35148434923192 - type: euclidean_pearson value: 81.71127467071571 - type: euclidean_spearman value: 82.88240481546771 - type: manhattan_pearson value: 81.72826221967252 - type: manhattan_spearman value: 82.90725064625128 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: e2125984e7df8b7871f6ae9949cf6b6795e7c54b metrics: - type: cos_sim_pearson value: 83.1474704168523 - type: cos_sim_spearman value: 79.20612995350827 - type: euclidean_pearson value: 78.85993329596555 - type: euclidean_spearman value: 78.91956572744715 - type: manhattan_pearson value: 78.89999720522347 - type: manhattan_spearman value: 78.93956842550107 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: 1cd7298cac12a96a373b6a2f18738bb3e739a9b6 metrics: - type: cos_sim_pearson value: 84.81255514055894 - type: cos_sim_spearman value: 85.5217140762934 - type: euclidean_pearson value: 82.15024353784499 - type: euclidean_spearman value: 83.04155334389833 - type: manhattan_pearson value: 82.18598945053624 - type: manhattan_spearman value: 83.07248357693301 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 360a0b2dff98700d09e634a01e1cc1624d3e42cd metrics: - type: cos_sim_pearson value: 80.63248465157822 - type: cos_sim_spearman value: 82.53853238521991 - type: euclidean_pearson value: 78.33936863828221 - type: euclidean_spearman value: 79.16305579487414 - type: manhattan_pearson value: 78.3888359870894 - type: manhattan_spearman value: 79.18504473136467 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: 9fc37e8c632af1c87a3d23e685d49552a02582a0 metrics: - type: cos_sim_pearson value: 90.09066290639687 - type: cos_sim_spearman value: 90.43893699357069 - type: euclidean_pearson value: 82.39520777222396 - type: euclidean_spearman value: 81.23948185395952 - type: manhattan_pearson value: 82.35529784653383 - type: manhattan_spearman value: 81.12681522483975 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 2de6ce8c1921b71a755b262c6b57fef195dd7906 metrics: - type: cos_sim_pearson value: 63.52752323046846 - type: cos_sim_spearman value: 63.19719780439462 - type: euclidean_pearson value: 58.29085490641428 - type: euclidean_spearman value: 58.975178656335046 - type: manhattan_pearson value: 58.183542772416985 - type: manhattan_spearman value: 59.190630462178994 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: 8913289635987208e6e7c72789e4be2fe94b6abd metrics: - type: cos_sim_pearson value: 85.45100366635687 - type: cos_sim_spearman value: 85.66816193002651 - type: euclidean_pearson value: 81.87976731329091 - type: euclidean_spearman value: 82.01382867690964 - type: manhattan_pearson value: 81.88260155706726 - type: manhattan_spearman value: 82.05258597906492 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: 56a6d0140cf6356659e2a7c1413286a774468d44 metrics: - type: map value: 77.53549990038017 - type: mrr value: 93.37474163454556 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: a75ae049398addde9b70f6b268875f5cbce99089 metrics: - type: map_at_1 value: 31.167 - type: map_at_10 value: 40.778 - type: map_at_100 value: 42.063 - type: map_at_1000 value: 42.103 - type: map_at_3 value: 37.12 - type: map_at_5 value: 39.205 - type: ndcg_at_1 value: 33.667 - type: ndcg_at_10 value: 46.662 - type: ndcg_at_100 value: 51.995999999999995 - type: ndcg_at_1000 value: 53.254999999999995 - type: ndcg_at_3 value: 39.397999999999996 - type: ndcg_at_5 value: 42.934 - type: precision_at_1 value: 33.667 - type: precision_at_10 value: 7.1 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 16.111 - type: precision_at_5 value: 11.600000000000001 - type: recall_at_1 value: 31.167 - type: recall_at_10 value: 63.744 - type: recall_at_100 value: 87.156 - type: recall_at_1000 value: 97.556 - type: recall_at_3 value: 44.0 - type: recall_at_5 value: 52.556000000000004 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: 5a8256d0dff9c4bd3be3ba3e67e4e70173f802ea metrics: - type: cos_sim_accuracy value: 99.55148514851486 - type: cos_sim_ap value: 80.535236573428 - type: cos_sim_f1 value: 75.01331912626532 - type: cos_sim_precision value: 80.27366020524515 - type: cos_sim_recall value: 70.39999999999999 - type: dot_accuracy value: 99.04851485148515 - type: dot_ap value: 28.505358821499726 - type: dot_f1 value: 36.36363636363637 - type: dot_precision value: 37.160751565762006 - type: dot_recall value: 35.6 - type: euclidean_accuracy value: 99.4990099009901 - type: euclidean_ap value: 74.95819047075476 - type: euclidean_f1 value: 71.15489874110564 - type: euclidean_precision value: 78.59733978234583 - type: euclidean_recall value: 65.0 - type: manhattan_accuracy value: 99.50198019801981 - type: manhattan_ap value: 75.02070096015086 - type: manhattan_f1 value: 71.20535714285712 - type: manhattan_precision value: 80.55555555555556 - type: manhattan_recall value: 63.800000000000004 - type: max_accuracy value: 99.55148514851486 - type: max_ap value: 80.535236573428 - type: max_f1 value: 75.01331912626532 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 70a89468f6dccacc6aa2b12a6eac54e74328f235 metrics: - type: v_measure value: 54.13314692311623 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: d88009ab563dd0b16cfaf4436abaf97fa3550cf0 metrics: - type: v_measure value: 31.115181648287145 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: ef807ea29a75ec4f91b50fd4191cb4ee4589a9f9 metrics: - type: map value: 44.771112666694336 - type: mrr value: 45.30415764790765 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: 8753c2788d36c01fc6f05d03fe3f7268d63f9122 metrics: - type: cos_sim_pearson value: 30.849429597669374 - type: cos_sim_spearman value: 30.384175038360194 - type: dot_pearson value: 29.030383429536823 - type: dot_spearman value: 28.03273624951732 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: 2c8041b2c07a79b6f7ba8fe6acc72e5d9f92d217 metrics: - type: map_at_1 value: 0.19499999999999998 - type: map_at_10 value: 1.0959999999999999 - type: map_at_100 value: 5.726 - type: map_at_1000 value: 13.611999999999998 - type: map_at_3 value: 0.45399999999999996 - type: map_at_5 value: 0.67 - type: ndcg_at_1 value: 71.0 - type: ndcg_at_10 value: 55.352999999999994 - type: ndcg_at_100 value: 40.797 - type: ndcg_at_1000 value: 35.955999999999996 - type: ndcg_at_3 value: 63.263000000000005 - type: ndcg_at_5 value: 60.14000000000001 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 56.99999999999999 - type: precision_at_100 value: 41.199999999999996 - type: precision_at_1000 value: 16.154 - type: precision_at_3 value: 66.667 - type: precision_at_5 value: 62.8 - type: recall_at_1 value: 0.19499999999999998 - type: recall_at_10 value: 1.3639999999999999 - type: recall_at_100 value: 9.317 - type: recall_at_1000 value: 33.629999999999995 - type: recall_at_3 value: 0.49300000000000005 - type: recall_at_5 value: 0.756 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b metrics: - type: map_at_1 value: 1.335 - type: map_at_10 value: 6.293 - type: map_at_100 value: 10.928 - type: map_at_1000 value: 12.359 - type: map_at_3 value: 3.472 - type: map_at_5 value: 4.935 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 16.178 - type: ndcg_at_100 value: 28.149 - type: ndcg_at_1000 value: 39.845000000000006 - type: ndcg_at_3 value: 19.171 - type: ndcg_at_5 value: 17.864 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 14.49 - type: precision_at_100 value: 6.306000000000001 - type: precision_at_1000 value: 1.3860000000000001 - type: precision_at_3 value: 21.088 - type: precision_at_5 value: 18.367 - type: recall_at_1 value: 1.335 - type: recall_at_10 value: 10.825999999999999 - type: recall_at_100 value: 39.251000000000005 - type: recall_at_1000 value: 74.952 - type: recall_at_3 value: 4.9110000000000005 - type: recall_at_5 value: 7.312 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 69.93339999999999 - type: ap value: 13.87476602492533 - type: f1 value: 53.867357615848555 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: 62146448f05be9e52a36b8ee9936447ea787eede metrics: - type: accuracy value: 62.43916242218449 - type: f1 value: 62.870386304954685 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4 metrics: - type: v_measure value: 37.202082549859796 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.65023544137807 - type: cos_sim_ap value: 65.99787692764193 - type: cos_sim_f1 value: 62.10650887573965 - type: cos_sim_precision value: 56.30901287553648 - type: cos_sim_recall value: 69.23482849604221 - type: dot_accuracy value: 79.10830303391549 - type: dot_ap value: 48.80109642320246 - type: dot_f1 value: 51.418744625967314 - type: dot_precision value: 40.30253107683091 - type: dot_recall value: 71.00263852242745 - type: euclidean_accuracy value: 82.45812719794957 - type: euclidean_ap value: 60.09969493259607 - type: euclidean_f1 value: 57.658573789246226 - type: euclidean_precision value: 55.62913907284768 - type: euclidean_recall value: 59.84168865435356 - type: manhattan_accuracy value: 82.46408773916671 - type: manhattan_ap value: 60.116199786815116 - type: manhattan_f1 value: 57.683903860160235 - type: manhattan_precision value: 53.41726618705036 - type: manhattan_recall value: 62.69129287598945 - type: max_accuracy value: 83.65023544137807 - type: max_ap value: 65.99787692764193 - type: max_f1 value: 62.10650887573965 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.34943920518494 - type: cos_sim_ap value: 84.5428891020442 - type: cos_sim_f1 value: 77.09709933923172 - type: cos_sim_precision value: 74.83150952967607 - type: cos_sim_recall value: 79.50415768401602 - type: dot_accuracy value: 84.53448208949432 - type: dot_ap value: 73.96328242371995 - type: dot_f1 value: 70.00553786515299 - type: dot_precision value: 63.58777665995976 - type: dot_recall value: 77.86418232214352 - type: euclidean_accuracy value: 86.87662514068381 - type: euclidean_ap value: 81.45499631520235 - type: euclidean_f1 value: 73.46567109816063 - type: euclidean_precision value: 69.71037533697381 - type: euclidean_recall value: 77.6485987064983 - type: manhattan_accuracy value: 86.88244654014825 - type: manhattan_ap value: 81.47180273946366 - type: manhattan_f1 value: 73.44624393136418 - type: manhattan_precision value: 70.80385852090032 - type: manhattan_recall value: 76.29350169387126 - type: max_accuracy value: 88.34943920518494 - type: max_ap value: 84.5428891020442 - type: max_f1 value: 77.09709933923172 --- # SGPT-5.8B-weightedmean-msmarco-specb-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 249592 with parameters: ``` {'batch_size': 2, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTJModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```

lewtun/my-awesome-setfit-model-3

lewtun

2022-10-03T09:06:25Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-03T09:06:17Z

--- 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 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## 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, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  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 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

tkubotake/distilbert-base-uncased-finetuned-emotion

tkubotake

2022-10-03T08:25:34Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-09-28T05:01:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.918 - name: F1 type: f1 value: 0.9179456491632857 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.918 - F1: 0.9179 ## 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: 64 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8566 | 1.0 | 250 | 0.3283 | 0.903 | 0.9002 | | 0.2607 | 2.0 | 500 | 0.2263 | 0.918 | 0.9179 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

PartiallyTyped/answerable_tydiqa_lm_pretrained_finnish

PartiallyTyped

2022-10-03T07:09:57Z

119

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "text generation", "fi", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-02T14:52:13Z

--- language: - fi tags: - text generation license: mit datasets: - answerable tydiqa --- # ReadMe This is a pretrained model based on [Finnish-NLP/gpt2-finnish](https://huggingface.co/Finnish-NLP/gpt2-finnish) that has been trained on [copenlu/answerable_tydiqa](https://huggingface.co/datasets/copenlu/answerable_tydiqa), specifically the text field of the Finnish samples for 2 epochs. To use the pretrained head, use: `AutoModelForCausalLM.from_pretrained`. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PartiallyTyped/answerable_tydiqa_lm_pretrained_finnish" model = AutoModelForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) ```

krm/my_exercice_mrpc

krm

2022-10-03T06:47:03Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-03T06:15:31Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: krm/my_exercice_mrpc results: [] ---  # krm/my_exercice_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6942 - Train Accuracy: 0.6200 - Validation Loss: 0.6486 - Validation Accuracy: 0.6838 - Epoch: 2 ## Model description Ce modèle n'est pas à utiliser. Il s'agit d'un petit essai. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.6860 | 0.6314 | 0.7727 | 0.6838 | 0 | | 0.6862 | 0.6347 | 0.6326 | 0.6838 | 1 | | 0.6942 | 0.6200 | 0.6486 | 0.6838 | 2 | ### Framework versions - Transformers 4.22.2 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1

sd-concepts-library/liminalspaces

sd-concepts-library

2022-10-03T04:23:32Z

0

2

null

[ "license:mit", "region:us" ]

null

2022-10-03T04:23:28Z

--- license: mit --- ### Liminalspaces on Stable Diffusion This is the `<liminal image>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<liminal image> 0](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/1.jpeg) ![<liminal image> 1](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/4.jpeg) ![<liminal image> 2](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/2.jpeg) ![<liminal image> 3](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/0.jpeg) ![<liminal image> 4](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/3.jpeg) ![<liminal image> 5](https://huggingface.co/sd-concepts-library/liminalspaces/resolve/main/concept_images/5.jpeg)

g30rv17ys/ddpm-geeve-cnv-1500-300ep

g30rv17ys

2022-10-03T01:51:32Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-10-02T16:17:04Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-geeve-cnv-1500-300ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-1500-300ep/tensorboard?#scalars)

Arman511/CatVsDog

Arman511

2022-10-03T00:02:30Z

0

fastai

[ "fastai", "region:us" ]

null

2022-10-03T00:01:08Z

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

din0s/t5-small-fr-finetuned-en-to-it

din0s

2022-10-02T22:46:16Z

112

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccmatrix", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-02T22:08:17Z

--- tags: - generated_from_trainer datasets: - ccmatrix metrics: - bleu model-index: - name: t5-small_fr-finetuned-en-to-it results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: ccmatrix type: ccmatrix config: en-it split: train[3000:12000] args: en-it metrics: - name: Bleu type: bleu value: 7.4222 ---  # t5-small_fr-finetuned-en-to-it This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-fr](https://huggingface.co/din0s/t5-small-finetuned-en-to-fr) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.3225 - Bleu: 7.4222 - Gen Len: 59.1127 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 94 | 3.0406 | 3.2546 | 52.6127 | | No log | 2.0 | 188 | 2.9278 | 3.1206 | 62.774 | | No log | 3.0 | 282 | 2.8573 | 3.4206 | 63.6707 | | No log | 4.0 | 376 | 2.8030 | 3.4847 | 66.408 | | No log | 5.0 | 470 | 2.7602 | 3.8933 | 64.362 | | 3.2982 | 6.0 | 564 | 2.7185 | 3.9298 | 66.058 | | 3.2982 | 7.0 | 658 | 2.6842 | 4.0344 | 65.5773 | | 3.2982 | 8.0 | 752 | 2.6536 | 4.3243 | 65.0047 | | 3.2982 | 9.0 | 846 | 2.6233 | 4.5078 | 64.5813 | | 3.2982 | 10.0 | 940 | 2.5966 | 4.6657 | 63.654 | | 2.9837 | 11.0 | 1034 | 2.5743 | 4.7664 | 63.326 | | 2.9837 | 12.0 | 1128 | 2.5526 | 4.9535 | 62.7327 | | 2.9837 | 13.0 | 1222 | 2.5303 | 5.1386 | 63.5887 | | 2.9837 | 14.0 | 1316 | 2.5122 | 5.1037 | 64.1667 | | 2.9837 | 15.0 | 1410 | 2.4937 | 5.3304 | 63.116 | | 2.8416 | 16.0 | 1504 | 2.4797 | 5.5006 | 61.4953 | | 2.8416 | 17.0 | 1598 | 2.4627 | 5.5892 | 62.01 | | 2.8416 | 18.0 | 1692 | 2.4497 | 5.8497 | 61.42 | | 2.8416 | 19.0 | 1786 | 2.4372 | 6.0074 | 61.1587 | | 2.8416 | 20.0 | 1880 | 2.4256 | 6.1464 | 60.522 | | 2.8416 | 21.0 | 1974 | 2.4148 | 6.3117 | 59.5567 | | 2.7428 | 22.0 | 2068 | 2.4039 | 6.4626 | 59.532 | | 2.7428 | 23.0 | 2162 | 2.3939 | 6.5287 | 60.2307 | | 2.7428 | 24.0 | 2256 | 2.3857 | 6.6093 | 60.22 | | 2.7428 | 25.0 | 2350 | 2.3772 | 6.8004 | 59.396 | | 2.7428 | 26.0 | 2444 | 2.3703 | 6.9433 | 59.5027 | | 2.6779 | 27.0 | 2538 | 2.3631 | 7.0153 | 59.1433 | | 2.6779 | 28.0 | 2632 | 2.3575 | 7.1783 | 58.9793 | | 2.6779 | 29.0 | 2726 | 2.3514 | 7.1639 | 59.362 | | 2.6779 | 30.0 | 2820 | 2.3457 | 7.2176 | 58.9927 | | 2.6779 | 31.0 | 2914 | 2.3411 | 7.2599 | 59.1433 | | 2.6335 | 32.0 | 3008 | 2.3374 | 7.284 | 59.1787 | | 2.6335 | 33.0 | 3102 | 2.3339 | 7.3678 | 59.07 | | 2.6335 | 34.0 | 3196 | 2.3307 | 7.3364 | 58.9813 | | 2.6335 | 35.0 | 3290 | 2.3281 | 7.3318 | 58.96 | | 2.6335 | 36.0 | 3384 | 2.3259 | 7.394 | 59.0787 | | 2.6335 | 37.0 | 3478 | 2.3245 | 7.4133 | 59.0393 | | 2.609 | 38.0 | 3572 | 2.3232 | 7.383 | 59.1887 | | 2.609 | 39.0 | 3666 | 2.3227 | 7.4105 | 59.1227 | | 2.609 | 40.0 | 3760 | 2.3225 | 7.4222 | 59.1127 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0

din0s/t5-small-de-finetuned-en-to-it

din0s

2022-10-02T22:43:18Z

108

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:ccmatrix", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-02T22:01:53Z

--- tags: - generated_from_trainer datasets: - ccmatrix metrics: - bleu model-index: - name: t5-small_de-finetuned-en-to-it results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: ccmatrix type: ccmatrix config: en-it split: train[3000:12000] args: en-it metrics: - name: Bleu type: bleu value: 6.7338 ---  # t5-small_de-finetuned-en-to-it This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-de](https://huggingface.co/din0s/t5-small-finetuned-en-to-de) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.3480 - Bleu: 6.7338 - Gen Len: 61.3273 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 94 | 3.1064 | 2.9057 | 47.5067 | | No log | 2.0 | 188 | 2.9769 | 2.7484 | 76.9273 | | No log | 3.0 | 282 | 2.9015 | 3.0624 | 79.8873 | | No log | 4.0 | 376 | 2.8444 | 3.2959 | 78.276 | | No log | 5.0 | 470 | 2.7989 | 3.6694 | 74.6013 | | 3.3505 | 6.0 | 564 | 2.7564 | 3.8098 | 74.3247 | | 3.3505 | 7.0 | 658 | 2.7212 | 3.9596 | 72.554 | | 3.3505 | 8.0 | 752 | 2.6886 | 4.2231 | 70.7673 | | 3.3505 | 9.0 | 846 | 2.6572 | 4.1466 | 72.0113 | | 3.3505 | 10.0 | 940 | 2.6294 | 4.2696 | 71.1647 | | 3.0254 | 11.0 | 1034 | 2.6064 | 4.6375 | 67.7707 | | 3.0254 | 12.0 | 1128 | 2.5838 | 4.7208 | 68.6707 | | 3.0254 | 13.0 | 1222 | 2.5614 | 4.9191 | 68.5767 | | 3.0254 | 14.0 | 1316 | 2.5427 | 4.9837 | 66.3867 | | 3.0254 | 15.0 | 1410 | 2.5241 | 5.1011 | 66.7667 | | 2.8789 | 16.0 | 1504 | 2.5093 | 5.283 | 64.944 | | 2.8789 | 17.0 | 1598 | 2.4919 | 5.3205 | 65.738 | | 2.8789 | 18.0 | 1692 | 2.4788 | 5.3046 | 65.3207 | | 2.8789 | 19.0 | 1786 | 2.4651 | 5.5282 | 64.9407 | | 2.8789 | 20.0 | 1880 | 2.4532 | 5.6745 | 63.0873 | | 2.8789 | 21.0 | 1974 | 2.4419 | 5.7073 | 63.4973 | | 2.7782 | 22.0 | 2068 | 2.4308 | 5.8513 | 62.8813 | | 2.7782 | 23.0 | 2162 | 2.4209 | 5.8267 | 64.1033 | | 2.7782 | 24.0 | 2256 | 2.4124 | 5.8534 | 64.2993 | | 2.7782 | 25.0 | 2350 | 2.4037 | 6.0406 | 63.8313 | | 2.7782 | 26.0 | 2444 | 2.3964 | 6.1517 | 63.4213 | | 2.7116 | 27.0 | 2538 | 2.3897 | 6.2175 | 63.0573 | | 2.7116 | 28.0 | 2632 | 2.3836 | 6.2551 | 62.876 | | 2.7116 | 29.0 | 2726 | 2.3777 | 6.4412 | 62.4167 | | 2.7116 | 30.0 | 2820 | 2.3717 | 6.4604 | 62.1087 | | 2.7116 | 31.0 | 2914 | 2.3673 | 6.5471 | 62.1373 | | 2.6662 | 32.0 | 3008 | 2.3634 | 6.5296 | 62.2533 | | 2.6662 | 33.0 | 3102 | 2.3596 | 6.6623 | 61.276 | | 2.6662 | 34.0 | 3196 | 2.3564 | 6.6591 | 61.392 | | 2.6662 | 35.0 | 3290 | 2.3539 | 6.7201 | 61.0827 | | 2.6662 | 36.0 | 3384 | 2.3516 | 6.675 | 61.3173 | | 2.6662 | 37.0 | 3478 | 2.3500 | 6.6894 | 61.3507 | | 2.6411 | 38.0 | 3572 | 2.3488 | 6.6539 | 61.5253 | | 2.6411 | 39.0 | 3666 | 2.3482 | 6.7135 | 61.3733 | | 2.6411 | 40.0 | 3760 | 2.3480 | 6.7338 | 61.3273 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0

ChrisC1657/Aqua_tests

ChrisC1657

2022-10-02T20:33:02Z

0

null

[ "license:mit", "region:us" ]

null

2022-10-02T17:06:56Z

--- license: mit --- Aqua_anime_girl_blk_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - black reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: Anime girl Aqua_animegirl_wrd_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - Waifu Research Department reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: Anime girl Aqua_CxRwMwCYViVz2JUH_blk_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - black reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: CxRwMwCYViVz2JUH Aqua_CxRwMwCYViVz2JUH_wrd_reg_2000.ckpt # Dataset >Training: 16 images >Regularization: 400 images - Waifu Research Department reg images # Info >Model Used: Waifu Diffusion 1.3 epoch 5 >Steps: 2000 >Keyword: Aqua (Use this in the prompt) >Class Phrase: CxRwMwCYViVz2JUH

waifu-research-department/Natsuki-Subaru

waifu-research-department

2022-10-02T20:26:18Z

0

4

null

[ "region:us" ]

null

2022-10-02T19:22:45Z

# Description Trainer: naotsue Natsuki Subaru from Re:Zero # Dataset >Training: 20 images >Regularization: 300 images # Info >Model Used: Waifu Diffusion 1.3 (Epoch 6) >Steps: 3000 >Keyword: SUBARU (Use this in the prompt) >Class Phrase: 1boy ![Sak](https://i.pinimg.com/originals/36/f9/c4/36f9c4b5813cb22aba08466fda544e27.png)

ericntay/stbl_clinical_bert_ft_rs9

ericntay

2022-10-02T19:02:19Z

116

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-02T18:44:01Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: stbl_clinical_bert_ft_rs9 results: [] ---  # stbl_clinical_bert_ft_rs9 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0866 - F1: 0.9109 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2712 | 1.0 | 101 | 0.0879 | 0.8420 | | 0.0666 | 2.0 | 202 | 0.0776 | 0.8726 | | 0.031 | 3.0 | 303 | 0.0630 | 0.8923 | | 0.015 | 4.0 | 404 | 0.0821 | 0.8958 | | 0.0087 | 5.0 | 505 | 0.0736 | 0.9084 | | 0.0061 | 6.0 | 606 | 0.0738 | 0.9083 | | 0.0037 | 7.0 | 707 | 0.0838 | 0.9157 | | 0.0027 | 8.0 | 808 | 0.0827 | 0.9088 | | 0.0017 | 9.0 | 909 | 0.0850 | 0.9135 | | 0.0014 | 10.0 | 1010 | 0.0871 | 0.9090 | | 0.001 | 11.0 | 1111 | 0.0868 | 0.9094 | | 0.001 | 12.0 | 1212 | 0.0866 | 0.9109 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1