license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
gpl-3.0 | [] | false | 训练过程 使用了[UER-py](https://github.com/dbiir/UER-py/) 进行fine-tuned 加入了包括但不限于摘要、负采样、混淆等数据加强方法 并转换为Huggingface进行上传 | | CMRC 2018 Dev | DRCD Dev | SQuAD-Zen Dev (Answerable) | AVG | | :-------: | :-----------: | :-------: | :------------------------: | :-------: | | PERT-large | 74.4/89.8 | 90.3/94.| 62.8/78.8 | 75.9/87.8 | | e4e5719cf0e8e59b977951ac89c76ca6 |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-nt5-small-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | 876b546c712b55518c3e3d0669d1efb3 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-test-ged-RAW_data_prep_2021_12_26___t1_7.csv_max_target_length_10 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0338 - Rouge1: 28.7359 - Rouge2: 15.6289 - Rougel: 28.6407 - Rougelsum: 28.7016 | c5f1cdfb29fa7ae9255a1829ce78c788 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 6.0554 | 1.0 | 1935 | 2.7346 | 23.7306 | 13.3598 | 23.7172 | 23.7447 | | 2.9111 | 2.0 | 3870 | 2.3916 | 26.5211 | 14.5628 | 26.4827 | 26.5716 | | 2.464 | 3.0 | 5805 | 2.2382 | 27.4404 | 15.1211 | 27.3331 | 27.401 | | 2.2328 | 4.0 | 7740 | 2.1557 | 28.3377 | 14.7406 | 28.2386 | 28.249 | | 2.0845 | 5.0 | 9675 | 2.1324 | 29.1476 | 15.7579 | 29.0614 | 29.1701 | | 1.9825 | 6.0 | 11610 | 2.0668 | 28.4677 | 15.3332 | 28.4128 | 28.4093 | | 1.9233 | 7.0 | 13545 | 2.0441 | 28.6832 | 15.5251 | 28.5723 | 28.6479 | | 1.8842 | 8.0 | 15480 | 2.0338 | 28.7359 | 15.6289 | 28.6407 | 28.7016 | | bd7193c79a0986eae3073947515d234d |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-fr_s156 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 00533ea2d59d4f1ef934e354453c1f8e |
apache-2.0 | ['text-generation'] | false | Model description [GPT-2](https://openai.com/blog/better-language-models/) is a large [transformer](https://arxiv.org/abs/1706.03762)-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. | efe9057ea5c9e07dfcfe09287bd1a2b6 |
apache-2.0 | ['text-generation'] | false | How to use For best experience and clean outputs, you can use Live Demo mentioned above, also you can use the notebook mentioned in my [GitHub](https://github.com/HamidRezaAttar/GPT2-Home) You can use this model directly with a pipeline for text generation. ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline >>> tokenizer = AutoTokenizer.from_pretrained("HamidRezaAttar/gpt2-product-description-generator") >>> model = AutoModelForCausalLM.from_pretrained("HamidRezaAttar/gpt2-product-description-generator") >>> generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100}) >>> generated_text = generator("This bed is very comfortable.") ``` | 55d000e6b40987017749149581086691 |
apache-2.0 | ['text-generation'] | false | Citation info ```bibtex @misc{GPT2-Home, author = {HamidReza Fatollah Zadeh Attar}, title = {GPT2-Home the English home product description generator}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/HamidRezaAttar/GPT2-Home}}, } ``` | a89f338da7dc862c8b6050bc22d57e06 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-sst2-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.2580 | 558d7695811bb96272da8e5147daafeb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.0249 | 0.4 | 500 | 7.2506 | | 7.1076 | 0.8 | 1000 | 7.1057 | | 6.8912 | 1.2 | 1500 | 7.2155 | | 6.8907 | 1.6 | 2000 | 7.3149 | | 6.8295 | 2.0 | 2500 | 7.2580 | | 966bd9b03cf274ea88de75f54f5b22ba |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s271 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 2e49ad81e39af70619c2884520761231 |
apache-2.0 | ['generated_from_keras_callback'] | false | test 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: | f7ae8ddc1bc8aa41165a46f813df7cab |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ru-finetuned-en-to-ru-Legal This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8561 - Bleu: 46.7284 - Gen Len: 23.1317 | dc65ec298ee9fbc705ac908eaa0cdac0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 387 | 1.1719 | 34.0562 | 22.991 | | 1.524 | 2.0 | 774 | 1.0342 | 37.7233 | 23.0052 | | 1.0226 | 3.0 | 1161 | 0.9595 | 40.0983 | 22.9755 | | 0.8066 | 4.0 | 1548 | 0.9188 | 41.9634 | 23.1162 | | 0.8066 | 5.0 | 1935 | 0.8907 | 43.6537 | 23.0923 | | 0.6637 | 6.0 | 2322 | 0.8771 | 44.5208 | 23.1097 | | 0.5697 | 7.0 | 2709 | 0.8669 | 45.5589 | 23.1388 | | 0.5175 | 8.0 | 3096 | 0.8603 | 46.2211 | 23.2356 | | 0.5175 | 9.0 | 3483 | 0.8566 | 46.7201 | 23.1375 | | 0.4768 | 10.0 | 3870 | 0.8561 | 46.7284 | 23.1317 | | e4e20bcc414593654477ee3569badb96 |
apache-2.0 | ['generated_from_keras_callback'] | false | Gorenzelg/bert-finetuned-squad11 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0664 - Epoch: 0 | f048baad276e5582a2495040beb9767a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 7a80987d639667befc6d85b80abefb3f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 192 - eval_batch_size: 192 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 | 125b9b8d401d48dfbd9e4686fb24f085 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | whisper-small-af-za - Ari This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0002 - eval_wer: 0.0 - eval_runtime: 77.0592 - eval_samples_per_second: 2.569 - eval_steps_per_second: 0.324 - epoch: 14.6 - step: 2000 | 190e284617306b5c6b8f442759482ec9 |
mit | [] | false | Jamiels on Stable Diffusion This is the `<jamiels>` 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`:       | 98dc9b255a3a60c7ab5703682a7db430 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mrpc_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6089 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 | ae6bbffbfd4caa01d3b3d1009ab1c73f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6363 | 1.0 | 15 | 0.6257 | 0.6838 | 0.8122 | 0.7480 | | 0.6306 | 2.0 | 30 | 0.6230 | 0.6838 | 0.8122 | 0.7480 | | 0.6302 | 3.0 | 45 | 0.6227 | 0.6838 | 0.8122 | 0.7480 | | 0.6217 | 4.0 | 60 | 0.6089 | 0.6838 | 0.8122 | 0.7480 | | 0.5729 | 5.0 | 75 | 0.6097 | 0.6838 | 0.7817 | 0.7328 | | 0.4868 | 6.0 | 90 | 0.6395 | 0.6789 | 0.7791 | 0.7290 | | 0.3906 | 7.0 | 105 | 0.7014 | 0.6838 | 0.7725 | 0.7282 | | 0.3014 | 8.0 | 120 | 0.7773 | 0.6814 | 0.7735 | 0.7274 | | 0.2538 | 9.0 | 135 | 0.8550 | 0.6789 | 0.7730 | 0.7259 | | 26ba61de9a617343156b5d303a3525d7 |
mit | ['generated_from_trainer'] | false | Facebook_Mit_HPS_5_Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4774 - Accuracy: 0.9315 | c125971b504bbc3fa730470178aa40cc |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.546392051994155e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 09b4c1cf25257fd0972d92f0639198ff |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 292 | 0.2181 | 0.9264 | | 0.2411 | 2.0 | 584 | 0.2571 | 0.9289 | | 0.2411 | 3.0 | 876 | 0.5712 | 0.8947 | | 0.0558 | 4.0 | 1168 | 0.4675 | 0.9332 | | 0.0558 | 5.0 | 1460 | 0.4774 | 0.9315 | | d792a285271baa6d05fee009565be75c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'textual-inversion', 'embedding'] | false | about - 2 embeddings to resemble a popular toy from the 90s - check the [PDF](https://huggingface.co/proxima/foorby/blob/main/foorby_embeddings_handbook.pdf) for comparisons, prompts and settings - v2 seems to trend more towards realism [<img src="https://huggingface.co/proxima/foorby/resolve/main/example_2.jpg">](https://huggingface.co/proxima/foorby/blob/main/example_2.jpg) | ae8b2f1872501141c9924ea3ce01695d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'textual-inversion', 'embedding'] | false | how to use - place the .bin files in your embeddings folder - use foorbyv1 or foorbyv2 in your prompt ---- if you enjoy this consider buying me a coffee (ノ◕ヮ◕)ノ*:・゚✧ <a href='https://ko-fi.com/S6S6FUYKY' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi3.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> ---- | 44ff0f9e026db186a7b0cddd9dcce427 |
cc-by-sa-4.0 | [] | false | ELECTRA base Japanese discriminator This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language. The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). | 59f7dcaa28411406408498784bad3f9f |
cc-by-sa-4.0 | [] | false | Model architecture The model architecture is the same as ELECTRA base in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 768 dimensions of hidden states, and 12 attention heads. | 9d29a88e64981c71fa9d1551397741aa |
cc-by-sa-4.0 | [] | false | Training The models are trained with the same configuration as ELECTRA base in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 512 tokens per instance, 256 instances per batch, and 766k training steps. The size of the generator is 1/3 of the size of the discriminator. | cb53086d7147e63ec34f9987347fa1d6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5953 - Rouge1: 0.1383 - Rouge2: 0.0487 - Rougel: 0.1135 - Rougelsum: 0.1132 - Gen Len: 19.0 | f6acd924d64136143196daa2cf831a4e |
apache-2.0 | ['generated_from_trainer'] | false | 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: 2 - mixed_precision_training: Native AMP | d403cf4bf8674899ac5b1d1a814a5dae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 124 | 2.6810 | 0.1312 | 0.0415 | 0.1076 | 0.1077 | 19.0 | | No log | 2.0 | 248 | 2.5953 | 0.1383 | 0.0487 | 0.1135 | 0.1132 | 19.0 | | a7814782a6260b1983a3aac8125b2cac |
apache-2.0 | ['automatic-speech-recognition'] | false | wav2vec2-xlsr-korean-senior Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) using the [AIhub 자유대화 음성(노인남녀)](https://aihub.or.kr/aidata/30704). - Total train data size: 808,642 - Total vaild data size: 159,970 When using this model, make sure that your speech input is sampled at 16kHz. The script used for training can be found here: https://github.com/hyyoka/wav2vec2-korean-senior | e6cae68e7dec1f1d22e8d66890fc24c4 |
apache-2.0 | ['automatic-speech-recognition'] | false | Inference ``` py import torchaudio from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import re def clean_up(transcription): hangul = re.compile('[^ ㄱ-ㅣ가-힣]+') result = hangul.sub('', transcription) return result model_name "hyyoka/wav2vec2-xlsr-korean-senior" processor = Wav2Vec2Processor.from_pretrained(model_name) model = Wav2Vec2ForCTC.from_pretrained(model_name) speech_array, sampling_rate = torchaudio.load(wav_file) feat = processor(speech_array[0], sampling_rate=16000, padding=True, max_length=800000, truncation=True, return_attention_mask=True, return_tensors="pt", pad_token_id=49 ) input = {'input_values': feat['input_values'],'attention_mask':feat['attention_mask']} outputs = model(**input, output_attentions=True) logits = outputs.logits predicted_ids = logits.argmax(axis=-1) transcription = processor.decode(predicted_ids[0]) stt_result = clean_up(transcription) ``` | e9a96a9af0725020feb296d410829909 |
apache-2.0 | [] | false | Introduction This seq-2-seq semantic parsing model is used by [Genie](https://github.com/stanford-oval/genie-toolkit) to compile an assistant in the restaurant domain. This model translates natural language utterances to [ThingTalk](https://github.com/stanford-oval/thingtalk), executed by Genie. | 70d56b2c8c2bb1ec4d2270840cc45148 |
apache-2.0 | ['translation'] | false | epo-ell * source group: Esperanto * target group: Modern Greek (1453-) * OPUS readme: [epo-ell](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ell/README.md) * model: transformer-align * source language(s): epo * target language(s): ell * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ell/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ell/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ell/opus-2020-06-16.eval.txt) | 29d6b824769bea3008d796f91221ba79 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-ell - source_languages: epo - target_languages: ell - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-ell/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'el'] - src_constituents: {'epo'} - tgt_constituents: {'ell'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ell/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/epo-ell/opus-2020-06-16.test.txt - src_alpha3: epo - tgt_alpha3: ell - short_pair: eo-el - chrF2_score: 0.43799999999999994 - bleu: 23.2 - brevity_penalty: 0.9159999999999999 - ref_len: 3892.0 - src_name: Esperanto - tgt_name: Modern Greek (1453-) - train_date: 2020-06-16 - src_alpha2: eo - tgt_alpha2: el - prefer_old: False - long_pair: epo-ell - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 65addbf753d9ff63efac3f9ea3632ed9 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.07-0.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8593 - Bleu: 7.0665 - Gen Len: 43.5793 | e0a137297ac774c626b249c4f53ca2ca |
apache-2.0 | ['generated_from_trainer'] | false | DistilGPT2-Beatles-Lyrics-finetuned This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [Huggingartists - beatles](https://huggingface.co/datasets/huggingartists/the-beatles) dataset. It will complete an input prompt with Beatles-like text. | 93b8ea185ee362d554c2d9fd5e2afae7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - 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: 100 - num_epochs: 5 | c45d772309a55fa78bf38d2c467f75ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.748 | 1.0 | 165 | 2.3732 | | 2.4395 | 2.0 | 330 | 2.1938 | | 2.2968 | 3.0 | 495 | 2.1118 | | 2.2075 | 4.0 | 660 | 2.0721 | | 2.1393 | 5.0 | 825 | 2.0571 | | 4f2f555afdaf45d6062c0a14f5cc4f36 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-dutch-cased-finetuned-mBERT This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0898 - Precision: 0.7255 - Recall: 0.7255 - F1: 0.7255 - Accuracy: 0.9758 | 718424ff9fe18c7f5839b52b036aafc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1603 | 1.0 | 533 | 0.0928 | 0.6896 | 0.6962 | 0.6929 | 0.9742 | | 0.0832 | 2.0 | 1066 | 0.0898 | 0.7255 | 0.7255 | 0.7255 | 0.9758 | | 96699c6c2aad0b5f399060c795a46b6f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion 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: 1.0202 - Accuracy: 0.8235 - F1: 0.8223 | 3c144d5d12b553a88f142dbe0431bc2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7099 | 1.0 | 17 | 0.6695 | 0.5294 | 0.3665 | | 0.686 | 2.0 | 34 | 0.6288 | 0.5294 | 0.3665 | | 0.5945 | 3.0 | 51 | 0.4339 | 0.8824 | 0.8824 | | 0.3718 | 4.0 | 68 | 0.3600 | 0.8235 | 0.8235 | | 0.1248 | 5.0 | 85 | 0.5730 | 0.8235 | 0.8223 | | 0.0984 | 6.0 | 102 | 0.7659 | 0.7647 | 0.7647 | | 0.0138 | 7.0 | 119 | 0.8271 | 0.8235 | 0.8223 | | 0.0121 | 8.0 | 136 | 0.8223 | 0.8235 | 0.8223 | | 0.0062 | 9.0 | 153 | 0.7349 | 0.8235 | 0.8223 | | 0.0045 | 10.0 | 170 | 0.8381 | 0.7647 | 0.7597 | | 0.0037 | 11.0 | 187 | 0.8636 | 0.7647 | 0.7597 | | 0.0031 | 12.0 | 204 | 0.8603 | 0.8235 | 0.8223 | | 0.0025 | 13.0 | 221 | 0.8714 | 0.8235 | 0.8223 | | 0.0021 | 14.0 | 238 | 0.8864 | 0.8235 | 0.8223 | | 0.002 | 15.0 | 255 | 0.9114 | 0.8235 | 0.8223 | | 0.0017 | 16.0 | 272 | 0.9295 | 0.8235 | 0.8223 | | 0.0014 | 17.0 | 289 | 0.9360 | 0.8235 | 0.8223 | | 0.0013 | 18.0 | 306 | 0.9378 | 0.8235 | 0.8223 | | 0.0012 | 19.0 | 323 | 0.9429 | 0.8235 | 0.8223 | | 0.0012 | 20.0 | 340 | 0.9528 | 0.8235 | 0.8223 | | 0.0011 | 21.0 | 357 | 0.9609 | 0.8235 | 0.8223 | | 0.001 | 22.0 | 374 | 0.9667 | 0.8235 | 0.8223 | | 0.001 | 23.0 | 391 | 0.9738 | 0.8235 | 0.8223 | | 0.001 | 24.0 | 408 | 0.9804 | 0.8235 | 0.8223 | | 0.0009 | 25.0 | 425 | 0.9827 | 0.8235 | 0.8223 | | 0.0009 | 26.0 | 442 | 0.9863 | 0.8235 | 0.8223 | | 0.0008 | 27.0 | 459 | 0.9910 | 0.8235 | 0.8223 | | 0.0008 | 28.0 | 476 | 0.9949 | 0.8235 | 0.8223 | | 0.0007 | 29.0 | 493 | 1.0002 | 0.8235 | 0.8223 | | 0.0008 | 30.0 | 510 | 1.0042 | 0.8235 | 0.8223 | | 0.0007 | 31.0 | 527 | 1.0058 | 0.8235 | 0.8223 | | 0.0007 | 32.0 | 544 | 1.0091 | 0.8235 | 0.8223 | | 0.0006 | 33.0 | 561 | 1.0118 | 0.8235 | 0.8223 | | 0.0006 | 34.0 | 578 | 1.0148 | 0.8235 | 0.8223 | | 0.0007 | 35.0 | 595 | 1.0163 | 0.8235 | 0.8223 | | 0.0006 | 36.0 | 612 | 1.0174 | 0.8235 | 0.8223 | | 0.0006 | 37.0 | 629 | 1.0185 | 0.8235 | 0.8223 | | 0.0006 | 38.0 | 646 | 1.0194 | 0.8235 | 0.8223 | | 0.0006 | 39.0 | 663 | 1.0200 | 0.8235 | 0.8223 | | 0.0006 | 40.0 | 680 | 1.0202 | 0.8235 | 0.8223 | | d812666512ab3f48eebb83487a4ef242 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_44 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4608 - Accuracy: 0.8270 - Macro-f1: 0.8253 - Weighted-macro-f1: 0.8274 | 86ae57da8d28c2485ce9b2bfca9ceebe |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-modelo-becas0 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv3 dataset. It achieves the following results on the evaluation set: - Loss: 3.1182 | b93a55e3466c5ee64bfdeab9725bb74f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 5 | 5.5381 | | No log | 2.0 | 10 | 4.9493 | | No log | 3.0 | 15 | 4.4985 | | No log | 4.0 | 20 | 4.1063 | | No log | 5.0 | 25 | 3.7708 | | No log | 6.0 | 30 | 3.5205 | | No log | 7.0 | 35 | 3.3313 | | No log | 8.0 | 40 | 3.2195 | | No log | 9.0 | 45 | 3.1453 | | No log | 10.0 | 50 | 3.1182 | | e4fd3b769f924633877b3285960d2580 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-010099-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8127 - Bleu: 7.735 - Gen Len: 44.5453 | 68eea03b90b2dc98ff1111b5a0d2aba3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1216 - F1: 0.8749 | f0089a2f529b32bfeea03873af596033 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2247 | 1.0 | 834 | 0.1429 | 0.8432 | | 0.1127 | 2.0 | 1668 | 0.1270 | 0.8653 | | 0.0712 | 3.0 | 2502 | 0.1216 | 0.8749 | | 0466e7b315a4dffd43469455ae613ae1 |
apache-2.0 | ['generated_from_trainer'] | false | model-1-reverse-bart This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3347 - Rouge1: 95.4467 - Rouge2: 91.7522 - Rougel: 95.448 - Rougelsum: 95.4377 - Gen Len: 15.5478 | 4ede4dbd0c2766955e53bc3193877ac7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | 0.0744 | 1.0 | 28039 | 0.3347 | 95.4467 | 91.7522 | 95.448 | 95.4377 | 15.5478 | | dffcc4d31c0b6420789913a269ab510b |
mit | [] | false | Andrej-sternen on Stable Diffusion This is the `<andrej-sternen>` 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`:     | fd6fabda922994ccc5babf6da80becc4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3830 - Wer: 19.5173 | 520af1647738b24d3fca510de1029a38 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.011 | 4.01 | 1000 | 0.3234 | 20.5978 | | 0.0011 | 8.03 | 2000 | 0.3650 | 19.4070 | | 0.0006 | 12.04 | 3000 | 0.3830 | 19.5173 | | 6e7bd1d3a7efabfdd94f5697be056e19 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-peyma-fa 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: 0.0937 - F1: 0.9249 | 0f8bee8d5f831b9c7c873b6e8ec161fd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1562 | 1.0 | 998 | 0.0691 | 0.8777 | | 0.0638 | 2.0 | 1996 | 0.0703 | 0.8908 | | 0.0457 | 3.0 | 2994 | 0.0645 | 0.8975 | | 0.0281 | 4.0 | 3992 | 0.0842 | 0.8994 | | 0.0206 | 5.0 | 4990 | 0.0651 | 0.9164 | | 0.0139 | 6.0 | 5988 | 0.0787 | 0.9148 | | 0.0083 | 7.0 | 6986 | 0.0838 | 0.9253 | | 0.0052 | 8.0 | 7984 | 0.0833 | 0.9221 | | 0.0031 | 9.0 | 8982 | 0.0947 | 0.9230 | | 0.0028 | 10.0 | 9980 | 0.0937 | 0.9249 | | 4b6c295ca2949e15a521b560449b90e2 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-marathi 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. It achieves the following results on the evaluation set: - Loss: 0.5656 - Wer: 0.2156 | fa6fc2b9415c001cf73d803faa90ae70 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-squad-qg-no-answer` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph). | 2c5533e7ae1bf34aeb3025192c1845dc |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-answer") output = pipe("generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>") ``` | e0ec5ba14b409b99a7a74ed81b40f623 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 89.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 53.37 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 36.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 27.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 21.12 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 23.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 62.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 47.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | 753bd522b97d14aebc0253cc32023284 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-small-squad-qg-no-answer/raw/main/trainer_config.json). | ec2fae0bd6dbb391b9a93d898b802d64 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3209 - Accuracy: 0.9429 | d027605c5771711686ed5efc8ed0e03d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | e7e56a522eb3eaaeab5ebe3f22b3210a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.0228 | 1.0 | 318 | 2.2545 | 0.7548 | | 1.7605 | 2.0 | 636 | 1.2040 | 0.8513 | | 0.959 | 3.0 | 954 | 0.6910 | 0.9123 | | 0.5707 | 4.0 | 1272 | 0.4821 | 0.9294 | | 0.3877 | 5.0 | 1590 | 0.3890 | 0.9394 | | 0.3025 | 6.0 | 1908 | 0.3476 | 0.9410 | | 0.258 | 7.0 | 2226 | 0.3264 | 0.9432 | | 0.2384 | 8.0 | 2544 | 0.3209 | 0.9429 | | 0667926851dee8ba472d49b486df1b8c |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Training Data This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. | e9833aec70e8adc62b2261614f1ce664 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | 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 CrossEncoder model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', max_length=128) scores = model.predict([('Un avion est en train de décoller.', "Un homme joue d'une grande flûte."), ("Un homme étale du fromage râpé sur une pizza.", "Une personne jette un chat au plafond") ]) ``` | 151a7d6691c63bcd4543e556dbd1ad34 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Evaluation The model can be evaluated as follows on the French test data of stsb. ```python from sentence_transformers.readers import InputExample from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator from datasets import load_dataset def convert_dataset(dataset): dataset_samples=[] for df in dataset: score = float(df['similarity_score'])/5.0 | c4ad19240ffaf4c2f40e248de3733a97 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | For Test set test_samples = convert_dataset(df_test) test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test') test_evaluator(models, output_path="./") ``` **Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correlation | Spearman correlation | | e93a74183ed33697168016ee7f8e3ab0 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | params | | ------------- | ------------- | ------------- |------------- | | [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M | - On test | Model | Pearson correlation | Spearman correlation | | ------------- | ------------- | ------------- | | [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57| | 04f86c6a1a66a28787c7b3a3c0c2457d |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['Sino-Tibetan_relations_during_the_Ming_dynasty', 'Human_Development_Index', 'Hunter-gatherer', 'Somalis', 'Black_people', 'Bird_migration', 'Biodiversity', 'Mammal', 'Predation', 'Botany', 'Heian_period', 'On_the_Origin_of_Species', 'Dominican_Order', 'Insect', 'Race_(human_categorization)', 'Neolithic', 'Sumer', 'Indigenous_peoples_of_the_Americas', 'Anthropology', 'Hunting'] - **Developed by:** nandysoham - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** [More Information Needed] | 93146730d98a0ef6bcfd674d512faaf0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-moral-ctx-action-conseq 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: 0.1111 - Accuracy: 0.9676 - F1: 0.9676 | d55e8e9e5e8f034970aefd067d9b6c59 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.989502318502869e-05 - train_batch_size: 2000 - eval_batch_size: 2000 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | e74370b368c24b31f05f110c50f9dd5d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 10 | 0.1569 | 0.9472 | 0.9472 | | No log | 2.0 | 20 | 0.1171 | 0.9636 | 0.9636 | | No log | 3.0 | 30 | 0.1164 | 0.9664 | 0.9664 | | No log | 4.0 | 40 | 0.1117 | 0.9672 | 0.9672 | | No log | 5.0 | 50 | 0.1111 | 0.9676 | 0.9676 | | ff340441ff0c54a1fd20e5b6415437cf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-triviaqa 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: 0.9949 | a07314bd5723cedf8e0694e60cb9c9a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0391 | 1.0 | 11195 | 1.0133 | | 0.8425 | 2.0 | 22390 | 0.9949 | | 9df81c54d35bc7fea7c77975cfb0db4b |
apache-2.0 | ['translation'] | false | opus-mt-pis-en * source languages: pis * target languages: en * OPUS readme: [pis-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pis-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pis-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pis-en/opus-2020-01-16.eval.txt) | 2aef69403ebc1d9f7e7e82bbbf59b5f0 |
apache-2.0 | ['translation'] | false | opus-mt-fi-he * source languages: fi * target languages: he * OPUS readme: [fi-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-he/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-he/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-he/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-he/opus-2020-01-08.eval.txt) | eaa794560600011de5c1edf9b001f8eb |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_unispeech_s833 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 8b2b9c42eaac1bb369ecee04d7731bd1 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/). | b22773e646cd5b981011a690e4e3bfa6 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos") s="国境の長いトンネルを抜けると雪国であった。" t=tokenizer.tokenize(s) p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(t,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` | 85ddf38792a2afd808c21fe6cfa4f9bb |
apache-2.0 | ['generated_from_trainer'] | false | xsun_models 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: 0.1191 - Accuracy: 1.0 | ffa7de8bb4ae90bf064a46fe50fdb0ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2054 | 1.0 | 1 | 0.1407 | 1.0 | | 0.1505 | 2.0 | 2 | 0.1191 | 1.0 | | 9ce4407d709d8ccb9918b7f553cb0afb |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/). | 7de17b2a320f05fe0833d92706179894 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(s,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-large-japanese-luw-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` | 5a0f85a850c0cb36b76c09ac60f222d6 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-qnli-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-qnli](https://huggingface.co/muhtasham/tiny-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0564 - Accuracy: 0.1268 | 5f75eb052cb4c01d3c15033c66c47d6b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6898 | 25.0 | 500 | 0.7650 | 0.2113 | | 0.663 | 50.0 | 1000 | 1.1165 | 0.1268 | | 0.6113 | 75.0 | 1500 | 1.6072 | 0.1127 | | 0.5491 | 100.0 | 2000 | 2.0564 | 0.1268 | | 4c5496cedba430e652e41892df849a56 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2205 - Accuracy: 0.923 - F1: 0.9231 | 0e0138f0965aad2ffa4662c615c67f4e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8625 | 1.0 | 250 | 0.3246 | 0.9075 | 0.9062 | | 0.2522 | 2.0 | 500 | 0.2205 | 0.923 | 0.9231 | | 233bc15bba494600764924cf66269964 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Model description The **roberta-large-bne-capitel-ner** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) large model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. | d2f0a6213107236cf0fc0fad13189c1a |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Intended uses and limitations **roberta-large-bne-capitel-ner** model can be used to recognize Named Entities (NE). The model is limited by its training dataset and may not generalize well for all use cases. | e1a7b537ebff8d674cd1cbb30cb4914d |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | How to use ```python from transformers import pipeline from pprint import pprint nlp = pipeline("ner", model="PlanTL-GOB-ES/roberta-large-bne-capitel-ner") example = "Me llamo Francisco Javier y vivo en Madrid." ner_results = nlp(example) pprint(ner_results) ``` | 099192c6a92743e3cda9a97618ca63ad |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Training procedure The model was trained with a batch size of 32 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. | a3070c26a9739f8088848fa9fa467449 |
apache-2.0 | ['national library of spain', 'spanish', 'bne', 'capitel', 'ner'] | false | Evaluation results We evaluated the **roberta-large-bne-capitel-ner** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines: | Model | CAPITEL-NERC (F1) | | ------------|:----| | roberta-large-bne-capitel-ner | **90.51** | | roberta-base-bne-capitel-ner | 89.60| | BETO | 87.72 | | mBERT | 88.10 | | BERTIN | 88.56 | | ELECTRA | 80.35 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish). | 428a68162b276bb169ac5251df320fdc |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny It 2 - Gianluca Ruberto This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.711485 - Wer: 43.392956 | 0ce177be244f204158922519017758c6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice. Unfortunately weight decay showed to have slightly worse result also on the evaluation dataset. | 9ac6c2c1b9d8f8798bf68b4568995131 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP - weight_decay: 0.3 | 5d843c73cd11b3b01ee87709a3eac1dd |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5837 | 0.95 | 1000 | 0.790046 | 50.6032 | | 0.4186 | 1.91 | 2000 | 0.730115 | 46.0067 | | 0.3154 | 2.86 | 3000 | 0.712776 | 44.114 | | 0.2676 | 3.82 | 4000 | 0.711485 | 43.393 | | 9fc95ef8dce9296a9186cc50086c817d |
apache-2.0 | ['generated_from_trainer'] | false | olm-bert-tiny-december-2022-target-glue-qnli This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6358 - Accuracy: 0.6306 | 90073810e4bed27e5c8a3501bfd59d7c |
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