modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
cointegrated/rubert-tiny-sentiment-balanced | [
"negative",
"neutral",
"positive"
] | ---
language: ["ru"]
tags:
- russian
- classification
- sentiment
- multiclass
widget:
- text: "Какая гадость эта ваша заливная рыба!"
---
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts.
The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`.
## Usage
The function below estimates the sentiment of the given text:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-tiny-sentiment-balanced'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
def get_sentiment(text, return_type='label'):
""" Calculate sentiment of a text. `return_type` can be 'label', 'score' or 'proba' """
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0]
if return_type == 'label':
return model.config.id2label[proba.argmax()]
elif return_type == 'score':
return proba.dot([-1, 0, 1])
return proba
text = 'Какая гадость эта ваша заливная рыба!'
# classify the text
print(get_sentiment(text, 'label')) # negative
# score the text on the scale from -1 (very negative) to +1 (very positive)
print(get_sentiment(text, 'score')) # -0.5894946306943893
# calculate probabilities of all labels
print(get_sentiment(text, 'proba')) # [0.7870447 0.4947824 0.19755007]
```
## Training
We trained the model on [the datasets collected by Smetanin](https://github.com/sismetanin/sentiment-analysis-in-russian). We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as [a Colab notebook](https://gist.github.com/avidale/e678c5478086c1d1adc52a85cb2b93e6). The metrics on the balanced test set are the following:
| Source | Macro F1 |
| ----------- | ----------- |
| SentiRuEval2016_banks | 0.83 |
| SentiRuEval2016_tele | 0.74 |
| kaggle_news | 0.66 |
| linis | 0.50 |
| mokoron | 0.98 |
| rureviews | 0.72 |
| rusentiment | 0.67 |
| 2,441 |
danielhou13/longformer-finetuned_papers_v2 | null | Entry not found | 15 |
michiyasunaga/LinkBERT-base | null | ---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
tags:
- bert
- exbert
- linkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
---
## LinkBERT-base
LinkBERT-base model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT).
## Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval).
## Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
### How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/LinkBERT-base')
model = AutoModel.from_pretrained('michiyasunaga/LinkBERT-base')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases.
## Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**General benchmarks ([MRQA](https://github.com/mrqa/MRQA-Shared-Task-2019) and [GLUE](https://gluebenchmark.com/)):**
| | HotpotQA | TriviaQA | SearchQA | NaturalQ | NewsQA | SQuAD | GLUE |
| ---------------------- | -------- | -------- | -------- | -------- | ------ | ----- | -------- |
| | F1 | F1 | F1 | F1 | F1 | F1 | Avg score |
| BERT-base | 76.0 | 70.3 | 74.2 | 76.5 | 65.7 | 88.7 | 79.2 |
| **LinkBERT-base** | **78.2** | **73.9** | **76.8** | **78.3** | **69.3** | **90.1** | **79.6** |
| BERT-large | 78.1 | 73.7 | 78.3 | 79.0 | 70.9 | 91.1 | 80.7 |
| **LinkBERT-large** | **80.8** | **78.2** | **80.5** | **81.0** | **72.6** | **92.7** | **81.1** |
## Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {Association for Computational Linguistics (ACL)},
}
```
| 3,543 |
unitary/unbiased-toxic-roberta | [
"toxicity",
"severe_toxicity",
"christian",
"jewish",
"muslim",
"black",
"white",
"psychiatric_or_mental_illness",
"obscene",
"identity_attack",
"insult",
"threat",
"sexual_explicit",
"male",
"female",
"homosexual_gay_or_lesbian"
] | <div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers
</div>
## Description
Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification.
Built by [Laura Hanu](https://laurahanu.github.io/) at [Unitary](https://www.unitary.ai/), where we are working to stop harmful content online by interpreting visual content in context.
Dependencies:
- For inference:
- 🤗 Transformers
- ⚡ Pytorch lightning
- For training will also need:
- Kaggle API (to download data)
| Challenge | Year | Goal | Original Data Source | Detoxify Model Name | Top Kaggle Leaderboard Score | Detoxify Score
|-|-|-|-|-|-|-|
| [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) | 2018 | build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. | Wikipedia Comments | `original` | 0.98856 | 0.98636
| [Jigsaw Unintended Bias in Toxicity Classification](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) | 2019 | build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. | Civil Comments | `unbiased` | 0.94734 | 0.93639
| [Jigsaw Multilingual Toxic Comment Classification](https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification) | 2020 | build effective multilingual models | Wikipedia Comments + Civil Comments | `multilingual` | 0.9536 | 0.91655*
*Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available.
It is also noteworthy to mention that the top leadearboard scores have been achieved using model ensembles. The purpose of this library was to build something user-friendly and straightforward to use.
## Limitations and ethical considerations
If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
The intended use of this library is for research purposes, fine-tuning on carefully constructed datasets that reflect real world demographics and/or to aid content moderators in flagging out harmful content quicker.
Some useful resources about the risk of different biases in toxicity or hate speech detection are:
- [The Risk of Racial Bias in Hate Speech Detection](https://homes.cs.washington.edu/~msap/pdfs/sap2019risk.pdf)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://arxiv.org/pdf/1703.04009.pdf%201.pdf)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://arxiv.org/pdf/1905.12516.pdf)
## Quick prediction
The `multilingual` model has been trained on 7 different languages so it should only be tested on: `english`, `french`, `spanish`, `italian`, `portuguese`, `turkish` or `russian`.
```bash
# install detoxify
pip install detoxify
```
```python
from detoxify import Detoxify
# each model takes in either a string or a list of strings
results = Detoxify('original').predict('example text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# optional to display results nicely (will need to pip install pandas)
import pandas as pd
print(pd.DataFrame(results, index=input_text).round(5))
```
For more details check the Prediction section.
## Labels
All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema:
- **Very Toxic** (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing your perspective)
- **Toxic** (a rude, disrespectful, or unreasonable comment that is somewhat likely to make you leave a discussion or give up on sharing your perspective)
- **Hard to Say**
- **Not Toxic**
More information about the labelling schema can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Toxic Comment Classification Challenge
This challenge includes the following labels:
- `toxic`
- `severe_toxic`
- `obscene`
- `threat`
- `insult`
- `identity_hate`
### Jigsaw Unintended Bias in Toxicity Classification
This challenge has 2 types of labels: the main toxicity labels and some additional identity labels that represent the identities mentioned in the comments.
Only identities with more than 500 examples in the test set (combined public and private) are included during training as additional labels and in the evaluation calculation.
- `toxicity`
- `severe_toxicity`
- `obscene`
- `threat`
- `insult`
- `identity_attack`
- `sexual_explicit`
Identity labels used:
- `male`
- `female`
- `homosexual_gay_or_lesbian`
- `christian`
- `jewish`
- `muslim`
- `black`
- `white`
- `psychiatric_or_mental_illness`
A complete list of all the identity labels available can be found [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data).
### Jigsaw Multilingual Toxic Comment Classification
Since this challenge combines the data from the previous 2 challenges, it includes all labels from above, however the final evaluation is only on:
- `toxicity`
## How to run
First, install dependencies
```bash
# clone project
git clone https://github.com/unitaryai/detoxify
# create virtual env
python3 -m venv toxic-env
source toxic-env/bin/activate
# install project
pip install -e detoxify
cd detoxify
# for training
pip install -r requirements.txt
```
## Prediction
Trained models summary:
|Model name| Transformer type| Data from
|:--:|:--:|:--:|
|`original`| `bert-base-uncased` | Toxic Comment Classification Challenge
|`unbiased`| `roberta-base`| Unintended Bias in Toxicity Classification
|`multilingual`| `xlm-roberta-base`| Multilingual Toxic Comment Classification
For a quick prediction can run the example script on a comment directly or from a txt containing a list of comments.
```bash
# load model via torch.hub
python run_prediction.py --input 'example' --model_name original
# load model from from checkpoint path
python run_prediction.py --input 'example' --from_ckpt_path model_path
# save results to a .csv file
python run_prediction.py --input test_set.txt --model_name original --save_to results.csv
# to see usage
python run_prediction.py --help
```
Checkpoints can be downloaded from the latest release or via the Pytorch hub API with the following names:
- `toxic_bert`
- `unbiased_toxic_roberta`
- `multilingual_toxic_xlm_r`
```bash
model = torch.hub.load('unitaryai/detoxify','toxic_bert')
```
Importing detoxify in python:
```python
from detoxify import Detoxify
results = Detoxify('original').predict('some text')
results = Detoxify('unbiased').predict(['example text 1','example text 2'])
results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])
# to display results nicely
import pandas as pd
print(pd.DataFrame(results,index=input_text).round(5))
```
## Training
If you do not already have a Kaggle account:
- you need to create one to be able to download the data
- go to My Account and click on Create New API Token - this will download a kaggle.json file
- make sure this file is located in ~/.kaggle
```bash
# create data directory
mkdir jigsaw_data
cd jigsaw_data
# download data
kaggle competitions download -c jigsaw-toxic-comment-classification-challenge
kaggle competitions download -c jigsaw-unintended-bias-in-toxicity-classification
kaggle competitions download -c jigsaw-multilingual-toxic-comment-classification
```
## Start Training
### Toxic Comment Classification Challenge
```bash
python create_val_set.py
python train.py --config configs/Toxic_comment_classification_BERT.json
```
### Unintended Bias in Toxicicity Challenge
```bash
python train.py --config configs/Unintended_bias_toxic_comment_classification_RoBERTa.json
```
### Multilingual Toxic Comment Classification
This is trained in 2 stages. First, train on all available data, and second, train only on the translated versions of the first challenge.
The [translated data](https://www.kaggle.com/miklgr500/jigsaw-train-multilingual-coments-google-api) can be downloaded from Kaggle in french, spanish, italian, portuguese, turkish, and russian (the languages available in the test set).
```bash
# stage 1
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR.json
# stage 2
python train.py --config configs/Multilingual_toxic_comment_classification_XLMR_stage2.json
```
### Monitor progress with tensorboard
```bash
tensorboard --logdir=./saved
```
## Model Evaluation
### Toxic Comment Classification Challenge
This challenge is evaluated on the mean AUC score of all the labels.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Unintended Bias in Toxicicity Challenge
This challenge is evaluated on a novel bias metric that combines different AUC scores to balance overall performance. More information on this metric [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation).
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
# to get the final bias metric
python model_eval/compute_bias_metric.py
```
### Multilingual Toxic Comment Classification
This challenge is evaluated on the AUC score of the main toxic label.
```bash
python evaluate.py --checkpoint saved/lightning_logs/checkpoints/example_checkpoint.pth --test_csv test.csv
```
### Citation
```
@misc{Detoxify,
title={Detoxify},
author={Hanu, Laura and {Unitary team}},
howpublished={Github. https://github.com/unitaryai/detoxify},
year={2020}
}
```
| 11,065 |
Xuhui/ToxDect-roberta-large | null | ---
language:
-
-
thumbnail:
tags:
-
-
-
license:
datasets:
-
-
metrics:
-
-
---
# Toxic language detection
## Model description
A toxic language detection model trained on tweets. The base model is Roberta-large. For more information,
including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='Xuhui/ToxDect-roberta-large', return_all_scores=True)
prediction = classifier("You are f**king stupid!", )
print(prediction)
"""
Output:
[[{'label': 'LABEL_0', 'score': 0.002632011892274022}, {'label': 'LABEL_1', 'score': 0.9973680377006531}]]
"""
```
## Training procedure
The random seed for this model is 22. For other details, please refer to the Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
### BibTeX entry and citation info
```bibtex
@inproceedings{zhou-etal-2020-debiasing,
title = {Challenges in Automated Debiasing for Toxic Language Detection},
author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah A.},
booktitle = {EACL},
abbr = {EACL},
html = {https://www.aclweb.org/anthology/2021.eacl-main.274.pdf},
code = {https://github.com/XuhuiZhou/Toxic_Debias},
year = {2021},
bibtex_show = {true},
selected = {true}
}
``` | 1,559 |
prajjwal1/bert-mini-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). These models are trained on MNLI.
If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
```
MNLI: 68.04%
MNLI-mm: 69.17%
```
These models were trained for 4 epochs.
[@prajjwal_1](https://twitter.com/prajjwal_1)
| 995 |
Jeevesh8/goog_bert_ft_cola-0 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-1 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-2 | null | Entry not found | 15 |
pysentimiento/bertweet-hate-speech | [
"aggressive",
"hateful",
"targeted"
] | ---
language:
- en
tags:
- twitter
- hate-speech
---
# Hate Speech detection in Spanish
## robertuito-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in English. Base model is [BERTweet](https://huggingface.co/vinai/bertweet-base), a RoBERTa model trained in English tweets.
It is a multi-classifier model, with the following classes:
- **HS**: is it hate speech?
- **TR**: is it targeted to a specific individual?
- **AG**: is it aggressive?
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Enjoy! 🤗
| 1,398 |
Jeevesh8/goog_bert_ft_cola-3 | null | Entry not found | 15 |
cross-encoder/nli-deberta-v3-large | [
"contradiction",
"entailment",
"neutral"
] | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-large
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on MNLI mismatched set: 90.49
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
``` | 2,784 |
gchhablani/bert-base-cased-finetuned-sst2 | [
"negative",
"positive"
] | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9231651376146789
---
<!-- 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-cased-finetuned-sst2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3649
- Accuracy: 0.9232
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.233 | 1.0 | 4210 | 0.9174 | 0.2841 |
| 0.1261 | 2.0 | 8420 | 0.9278 | 0.3310 |
| 0.0768 | 3.0 | 12630 | 0.9232 | 0.3649 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
| 2,648 |
IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment | null | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- Sentiment
inference: true
widget:
- text: "今天心情不好"
---
# Erlangshen-Roberta-330M-Semtiment, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 8 sentiment datasets in the Chinese domain for finetune, with a total of 227347 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext)
## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment')
text='今天心情不好'
output=model(torch.tensor([tokenizer.encode(text)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))
```
## Scores on downstream chinese tasks
| Model | ASAP-SENT | ASAP-ASPECT | ChnSentiCorp |
| :--------: | :-----: | :----: | :-----: |
| Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |
| Erlangshen-Roberta-330M-Sentiment | 97.9 | 97.51 | 96.66 |
| Erlangshen-MegatronBert-1.3B-Sentiment | 98.1 | 97.8 | 97 |
## Citation
If you find the resource is useful, please cite the following website in your paper.
```
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` | 1,535 |
Jeevesh8/goog_bert_ft_cola-4 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-5 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-6 | null | Entry not found | 15 |
cointegrated/rubert-base-cased-dp-paraphrase-detection | [
"entailment",
"not_entailment"
] | ---
language: ["ru"]
tags:
- sentence-similarity
- text-classification
datasets:
- merionum/ru_paraphraser
---
This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from http://paraphraser.ru/.
It classifies texts as paraphrases (class 1) or non-paraphrases (class 0).
```python
import torch
from transformers import AutoModelForSequenceClassification, BertTokenizer
model_name = 'cointegrated/rubert-base-cased-dp-paraphrase-detection'
model = AutoModelForSequenceClassification.from_pretrained(model_name).cuda()
tokenizer = BertTokenizer.from_pretrained(model_name)
def compare_texts(text1, text2):
batch = tokenizer(text1, text2, return_tensors='pt').to(model.device)
with torch.inference_mode():
proba = torch.softmax(model(**batch).logits, -1).cpu().numpy()
return proba[0] # p(non-paraphrase), p(paraphrase)
print(compare_texts('Сегодня на улице хорошая погода', 'Сегодня на улице отвратительная погода'))
# [0.7056226 0.2943774]
print(compare_texts('Сегодня на улице хорошая погода', 'Отличная погодка сегодня выдалась'))
# [0.16524374 0.8347562 ]
```
P.S. In the DeepPavlov repository, the tokenizer uses `max_seq_length=64`.
This model, however, uses `model_max_length=512`.
Therefore, the results on long texts may be inadequate. | 1,510 |
ml6team/distilbert-base-german-cased-toxic-comments | [
"non_toxic",
"toxic"
] | ---
language:
- de
tags:
- distilbert
- german
- classification
datasets:
- germeval21
widget:
- text: "Das ist ein guter Punkt, so hatte ich das noch nicht betrachtet."
example_title: "Agreement (non-toxic)"
- text: "Wow, was ein geiles Spiel. Glückwunsch."
example_title: "Football (non-toxic)"
- text: "Halt deine scheiß Fresse, du Arschloch"
example_title: "Silence (toxic)"
- text: "Verpiss dich, du dreckiger Hurensohn."
example_title: "Dismiss (toxic)"
---
# German Toxic Comment Classification
## Model Description
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we fine-tuned a German DistilBERT model [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on a combination of five German datasets containing toxicity, profanity, offensive, or hate speech.
## Intended Uses & Limitations
This model can be used to detect toxicity in German comments.
However, the definition of toxicity is vague and the model might not be able to detect all instances of toxicity.
It will not be able to detect toxicity in languages other than German.
## How to Use
```python
from transformers import pipeline
model_hub_url = 'https://huggingface.co/ml6team/distilbert-base-german-cased-toxic-comments'
model_name = 'ml6team/distilbert-base-german-cased-toxic-comments'
toxicity_pipeline = pipeline('text-classification', model=model_name, tokenizer=model_name)
comment = "Ein harmloses Beispiel"
result = toxicity_pipeline(comment)[0]
print(f"Comment: {comment}\nLabel: {result['label']}, score: {result['score']}")
```
## Limitations and Bias
The model was trained on a combinations of datasets that contain examples gathered from different social networks and internet communities. This only represents a narrow subset of possible instances of toxicity and instances in other domains might not be detected reliably.
## Training Data
The training dataset combines the following five datasets:
* GermEval18 [[dataset](https://github.com/uds-lsv/GermEval-2018-Data)]
* Labels: abuse, profanity, toxicity
* GermEval21 [[dataset](https://github.com/germeval2021toxic/SharedTask/tree/main/Data%20Sets)]
* Labels: toxicity
* IWG Hatespeech dataset [[paper](https://arxiv.org/pdf/1701.08118.pdf), [dataset](https://github.com/UCSM-DUE/IWG_hatespeech_public)]
* Labels: hate speech
* Detecting Offensive Statements Towards Foreigners in Social Media (2017) by Breitschneider and Peters [[dataset](http://ub-web.de/research/)]
* Labels: hate
* HASOC: 2019 Hate Speech and Offensive Content [[dataset](https://hasocfire.github.io/hasoc/2019/index.html)]
* Labels: offensive, profanity, hate
The datasets contains different labels ranging from profanity, over hate speech to toxicity. In the combined dataset these labels were subsumed as `toxic` and `non-toxic` and contains 23,515 examples in total.
Note that the datasets vary substantially in the number of examples.
## Training Procedure
The training and test set were created using either the predefined train/test splits where available and otherwise 80% of the examples for training and 20% for testing. This resulted in in 17,072 training examples and 6,443 test examples.
The model was trained for 2 epochs with the following arguments:
```python
training_args = TrainingArguments(
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=2,
evaluation_strategy="steps",
logging_strategy="steps",
logging_steps=100,
save_total_limit=5,
learning_rate=2e-5,
weight_decay=0.01,
metric_for_best_model='accuracy',
load_best_model_at_end=True
)
```
## Evaluation Results
Model evaluation was done on 1/10th of the dataset, which served as the test dataset.
| Accuracy | F1 Score | Recall | Precision |
| -------- | -------- | -------- | ----------- |
| 78.50 | 50.34 | 39.22 | 70.27 |
| 3,969 |
textattack/bert-base-uncased-RTE | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
| 622 |
Jeevesh8/goog_bert_ft_cola-7 | null | Entry not found | 15 |
ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli | [
"entailment",
"neutral",
"contradiction"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-8 | null | Entry not found | 15 |
howey/roberta-large-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-9 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-10 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-11 | null | Entry not found | 15 |
optimum/distilbert-base-uncased-finetuned-banking77 | [
"Refund_not_showing_up",
"activate_my_card",
"age_limit",
"apple_pay_or_google_pay",
"atm_support",
"automatic_top_up",
"balance_not_updated_after_bank_transfer",
"balance_not_updated_after_cheque_or_cash_deposit",
"beneficiary_not_allowed",
"cancel_transfer",
"card_about_to_expire",
"card_acceptance",
"card_arrival",
"card_delivery_estimate",
"card_linking",
"card_not_working",
"card_payment_fee_charged",
"card_payment_not_recognised",
"card_payment_wrong_exchange_rate",
"card_swallowed",
"cash_withdrawal_charge",
"cash_withdrawal_not_recognised",
"change_pin",
"compromised_card",
"contactless_not_working",
"country_support",
"declined_card_payment",
"declined_cash_withdrawal",
"declined_transfer",
"direct_debit_payment_not_recognised",
"disposable_card_limits",
"edit_personal_details",
"exchange_charge",
"exchange_rate",
"exchange_via_app",
"extra_charge_on_statement",
"failed_transfer",
"fiat_currency_support",
"get_disposable_virtual_card",
"get_physical_card",
"getting_spare_card",
"getting_virtual_card",
"lost_or_stolen_card",
"lost_or_stolen_phone",
"order_physical_card",
"passcode_forgotten",
"pending_card_payment",
"pending_cash_withdrawal",
"pending_top_up",
"pending_transfer",
"pin_blocked",
"receiving_money",
"request_refund",
"reverted_card_payment?",
"supported_cards_and_currencies",
"terminate_account",
"top_up_by_bank_transfer_charge",
"top_up_by_card_charge",
"top_up_by_cash_or_cheque",
"top_up_failed",
"top_up_limits",
"top_up_reverted",
"topping_up_by_card",
"transaction_charged_twice",
"transfer_fee_charged",
"transfer_into_account",
"transfer_not_received_by_recipient",
"transfer_timing",
"unable_to_verify_identity",
"verify_my_identity",
"verify_source_of_funds",
"verify_top_up",
"virtual_card_not_working",
"visa_or_mastercard",
"why_verify_identity",
"wrong_amount_of_cash_received",
"wrong_exchange_rate_for_cash_withdrawal"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
- f1
widget:
- text: Could you assist me in finding my lost card?
example_title: Example 1
- text: I found my lost card. Am I still able to use it?
example_title: Example 2
- text: "Hey, I thought my topup was all done but now the money is gone again \u2013\
\ what\u2019s up with that?"
example_title: Example 3
- text: "Tell me why my topup wouldn\u2019t go through?"
example_title: Example 4
model-index:
- name: distilbert-base-uncased-finetuned-banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.925018570680639
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.925
verified: true
- name: Precision Macro
type: precision
value: 0.9282769473964405
verified: true
- name: Precision Micro
type: precision
value: 0.925
verified: true
- name: Precision Weighted
type: precision
value: 0.9282769473964405
verified: true
- name: Recall Macro
type: recall
value: 0.9250000000000002
verified: true
- name: Recall Micro
type: recall
value: 0.925
verified: true
- name: Recall Weighted
type: recall
value: 0.925
verified: true
- name: F1 Macro
type: f1
value: 0.9250185706806391
verified: true
- name: F1 Micro
type: f1
value: 0.925
verified: true
- name: F1 Weighted
type: f1
value: 0.925018570680639
verified: true
- name: loss
type: loss
value: 0.2934279143810272
verified: true
---
<!-- 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-banking77
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2935
- Accuracy: 0.925
- F1: 0.9250
## 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: 9.686210354742596e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 40
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 126 | 1.1457 | 0.7896 | 0.7685 |
| No log | 2.0 | 252 | 0.4673 | 0.8906 | 0.8889 |
| No log | 3.0 | 378 | 0.3488 | 0.9150 | 0.9151 |
| 0.9787 | 4.0 | 504 | 0.3238 | 0.9180 | 0.9179 |
| 0.9787 | 5.0 | 630 | 0.3126 | 0.9225 | 0.9226 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
| 3,616 |
Jeevesh8/goog_bert_ft_cola-12 | null | Entry not found | 15 |
l-yohai/bigbird-roberta-base-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-13 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-14 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-16 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-15 | null | Entry not found | 15 |
j-hartmann/purchase-intention-english-roberta-large | [
"no",
"yes"
] | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "This looks tasty. Where can I buy it??"
- text: "Now I want this, too."
- text: "You look great today!"
- text: "I just love spring and sunshine!"
---
This RoBERTa-based model can classify *expressed purchase intentions* in English language text in 2 classes:
- purchase intention 🤩
- no purchase intention 😐
The model was fine-tuned on 2,000 manually annotated social media posts.
The hold-out accuracy is 95% (vs. a balanced 50% random-chance baseline).
For details on the training approach see Web Appendix F in Hartmann et al. (2021).
# Application
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/purchase-intention-english-roberta-large", return_all_scores=True)
classifier("I want this!")
```
```python
Output:
[[{'label': 'no', 'score': 0.0014553926885128021},
{'label': 'yes', 'score': 0.9985445737838745}]]
```
# Reference
Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [j.p.hartmann@rug.nl](mailto:j.p.hartmann@rug.nl) with any questions or feedback you may have.
```
@article{hartmann2021,
title={The Power of Brand Selfies},
author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
journal={Journal of Marketing Research}
year={2021}
}
``` | 1,427 |
deepset/bert-base-german-cased-hatespeech-GermEval18Coarse | [
"OFFENSE",
"OTHER"
] | ---
license: cc-by-4.0
---
This is a German BERT v1 (https://deepset.ai/german-bert) trained to do hate speech detection on the GermEval18Coarse dataset | 153 |
Jeevesh8/goog_bert_ft_cola-17 | null | Entry not found | 15 |
valurank/distilroberta-hatespeech | [
"HATE",
"NOT_HATE"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-hatespeech
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. -->
# distilroberta-hatespeech
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3619
- Acc: 0.8423
## 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: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3096 | 1.0 | 4021 | 0.3375 | 0.8540 |
| 0.3711 | 2.0 | 8042 | 0.3305 | 0.8574 |
| 0.322 | 3.0 | 12063 | 0.3398 | 0.8534 |
| 0.3197 | 4.0 | 16084 | 0.3444 | 0.8504 |
| 0.3332 | 5.0 | 20105 | 0.3619 | 0.8423 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,625 |
Jeevesh8/goog_bert_ft_cola-18 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-20 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-19 | null | Entry not found | 15 |
valurank/distilroberta-proppy | [
"no_prop",
"prop"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-proppy
results: []
---
# distilroberta-proppy
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the proppy corpus.
It achieves the following results on the evaluation set:
- Loss: 0.1838
- Acc: 0.9269
## Training and evaluation data
The training data is the [proppy corpus](https://zenodo.org/record/3271522). See [Proppy: Organizing the News
Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3179 | 1.0 | 732 | 0.2032 | 0.9146 |
| 0.2933 | 2.0 | 1464 | 0.2026 | 0.9206 |
| 0.2938 | 3.0 | 2196 | 0.1849 | 0.9252 |
| 0.3429 | 4.0 | 2928 | 0.1983 | 0.9221 |
| 0.2608 | 5.0 | 3660 | 0.2310 | 0.9106 |
| 0.2562 | 6.0 | 4392 | 0.1826 | 0.9270 |
| 0.2785 | 7.0 | 5124 | 0.1954 | 0.9228 |
| 0.307 | 8.0 | 5856 | 0.2056 | 0.9200 |
| 0.28 | 9.0 | 6588 | 0.1843 | 0.9259 |
| 0.2794 | 10.0 | 7320 | 0.1782 | 0.9299 |
| 0.2868 | 11.0 | 8052 | 0.1907 | 0.9242 |
| 0.2789 | 12.0 | 8784 | 0.2031 | 0.9216 |
| 0.2827 | 13.0 | 9516 | 0.1976 | 0.9229 |
| 0.2795 | 14.0 | 10248 | 0.1866 | 0.9255 |
| 0.2895 | 15.0 | 10980 | 0.1838 | 0.9269 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3
| 2,133 |
valurank/distilroberta-offensive | [
"NOT_OFFENSIVE",
"OFFENSIVE"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-offensive
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. -->
# distilroberta-offensive
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4526
- Acc: 0.8975
## 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: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2321 | 1.0 | 1030 | 0.2404 | 0.9044 |
| 0.2539 | 2.0 | 2060 | 0.2139 | 0.9098 |
| 0.1997 | 3.0 | 3090 | 0.2561 | 0.9090 |
| 0.1663 | 4.0 | 4120 | 0.2409 | 0.9030 |
| 0.1515 | 5.0 | 5150 | 0.3000 | 0.9055 |
| 0.1035 | 6.0 | 6180 | 0.4170 | 0.9027 |
| 0.0466 | 7.0 | 7210 | 0.4526 | 0.8975 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| 1,736 |
Jeevesh8/goog_bert_ft_cola-21 | null | Entry not found | 15 |
j-hartmann/emotion-english-roberta-large | [
"anger",
"disgust",
"fear",
"joy",
"neutral",
"sadness",
"surprise"
] | ---
language: "en"
tags:
- roberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
## Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets and predicts Ekman's 6 basic emotions, plus a neutral class:
1) anger 🤬
2) disgust 🤢
3) fear 😨
4) joy 😀
5) neutral 😐
6) sadness 😭
7) surprise 😲
The model is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large).
For further details on this emotion model, please refer to the model card of its [DistilRoBERTa](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) version. | 741 |
Jeevesh8/goog_bert_ft_cola-22 | null | Entry not found | 15 |
Harshveer/autonlp-formality_scoring_2-32597818 | [
"target"
] | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Harshveer/autonlp-data-formality_scoring_2
co2_eq_emissions: 8.655894631203154
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 32597818
- CO2 Emissions (in grams): 8.655894631203154
## Validation Metrics
- Loss: 0.5410276651382446
- MSE: 0.5410276651382446
- MAE: 0.5694561004638672
- R2: 0.6830431129198475
- RMSE: 0.735545814037323
- Explained Variance: 0.6834385395050049
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Harshveer/autonlp-formality_scoring_2-32597818
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Harshveer/autonlp-formality_scoring_2-32597818", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Harshveer/autonlp-formality_scoring_2-32597818", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | 1,201 |
Bhumika/roberta-base-finetuned-sst2 | null | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.944954128440367
---
<!-- 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. -->
# roberta-base-finetuned-sst2
This model was trained from scratch on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3000
- Accuracy: 0.9450
## 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.1106 | 1.0 | 4210 | 0.9255 | 0.3326 |
| 0.1497 | 2.0 | 8420 | 0.9369 | 0.2858 |
| 0.1028 | 3.0 | 12630 | 0.3128 | 0.9335 |
| 0.0872 | 4.0 | 16840 | 0.3000 | 0.9450 |
| 0.0571 | 5.0 | 21050 | 0.3378 | 0.9427 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| 1,755 |
Jeevesh8/goog_bert_ft_cola-23 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-24 | null | Entry not found | 15 |
valurank/distilroberta-propaganda-2class | [
"No_Prop",
"Prop"
] | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-propaganda-2class
results: []
---
# distilroberta-propaganda-2class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the QCRI propaganda dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5087
- Acc: 0.7424
## Training and evaluation data
Training data is the 19-class QCRI propaganda data, with all propaganda classes collapsed to a single catch-all 'prop' class.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5737 | 1.0 | 493 | 0.5998 | 0.6515 |
| 0.4954 | 2.0 | 986 | 0.5530 | 0.7080 |
| 0.4774 | 3.0 | 1479 | 0.5331 | 0.7258 |
| 0.4846 | 4.0 | 1972 | 0.5247 | 0.7339 |
| 0.4749 | 5.0 | 2465 | 0.5392 | 0.7199 |
| 0.502 | 6.0 | 2958 | 0.5124 | 0.7466 |
| 0.457 | 7.0 | 3451 | 0.5167 | 0.7432 |
| 0.4899 | 8.0 | 3944 | 0.5160 | 0.7428 |
| 0.4833 | 9.0 | 4437 | 0.5280 | 0.7339 |
| 0.5114 | 10.0 | 4930 | 0.5112 | 0.7436 |
| 0.4419 | 11.0 | 5423 | 0.5060 | 0.7525 |
| 0.4743 | 12.0 | 5916 | 0.5031 | 0.7547 |
| 0.4597 | 13.0 | 6409 | 0.5043 | 0.7517 |
| 0.4861 | 14.0 | 6902 | 0.5055 | 0.7487 |
| 0.499 | 15.0 | 7395 | 0.5091 | 0.7419 |
| 0.501 | 16.0 | 7888 | 0.5037 | 0.7521 |
| 0.4659 | 17.0 | 8381 | 0.5087 | 0.7424 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3
| 2,172 |
w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0 | null | ---
language: id
tags:
- indonesian-roberta-base-indolem-sentiment-classifier-fold-0
license: mit
datasets:
- indolem
widget:
- text: "Pelayanan hotel ini sangat baik."
---
## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indolem`](https://indolem.github.io/)'s [Sentiment Analysis](https://github.com/indolem/indolem/tree/main/sentiment) dataset consisting of Indonesian tweets and hotel reviews (Koto et al., 2020).
A 5-fold cross-validation experiment was performed, with splits provided by the original dataset authors. This model was trained on fold 0. You can find models trained on [fold 0](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0), [fold 1](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-1), [fold 2](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-2), [fold 3](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-3), and [fold 4](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-4), in their respective links.
On **fold 0**, the model achieved an F1 of 86.42% on dev/validation and 83.12% on test. On all **5 folds**, the models achieved an average F1 of 84.14% on dev/validation and 84.64% on test.
Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------------------------------- | ------- | ------------ | ------------------------------- |
| `indonesian-roberta-base-indolem-sentiment-classifier-fold-0` | 124M | RoBERTa Base | `IndoLEM`'s Sentiment Analysis |
## Evaluation Results
The model was trained for 10 epochs and the best model was loaded at the end.
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
| 1 | 0.563500 | 0.420457 | 0.796992 | 0.626728 | 0.680000 | 0.581197 |
| 2 | 0.293600 | 0.281360 | 0.884712 | 0.811475 | 0.779528 | 0.846154 |
| 3 | 0.163000 | 0.267922 | 0.904762 | 0.844262 | 0.811024 | 0.880342 |
| 4 | 0.090200 | 0.335411 | 0.899749 | 0.838710 | 0.793893 | 0.888889 |
| 5 | 0.065200 | 0.462526 | 0.897243 | 0.835341 | 0.787879 | 0.888889 |
| 6 | 0.039200 | 0.423001 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 7 | 0.025300 | 0.452100 | 0.912281 | 0.859438 | 0.810606 | 0.914530 |
| 8 | 0.010400 | 0.525200 | 0.914787 | 0.855932 | 0.848739 | 0.863248 |
| 9 | 0.007100 | 0.513585 | 0.909774 | 0.850000 | 0.829268 | 0.871795 |
| 10 | 0.007200 | 0.537254 | 0.917293 | 0.864198 | 0.833333 | 0.897436 |
## How to Use
### As Text Classifier
```python
from transformers import pipeline
pretrained_name = "w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0"
nlp = pipeline(
"sentiment-analysis",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("Pelayanan hotel ini sangat baik.")
```
## Disclaimer
Do consider the biases which come from both the pre-trained RoBERTa model and `IndoLEM`'s Sentiment Analysis dataset that may be carried over into the results of this model.
## Author
Indonesian RoBERTa Base IndoLEM Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access.
| 4,195 |
Jeevesh8/goog_bert_ft_cola-25 | null | Entry not found | 15 |
salesken/xlm-roberta-base-finetuned-mnli-cross-lingual-transfer | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
datasets:
- mnli
- xnli
tags:
- sentence-similarity
- transformers
- text-classification
- zero-shot-classification
- salesken
- hindi
- cross-lingual
inference: false
---
# XLM-R Base
A multilingual model is pre-trained on text coming from a mix of languages. We will look at a multilingual model called XLM-R from Facebook also called as Cross Lingual Model - Roberta. Unlike BERT which was pre-trained on English Wikipedia and BookCorpus, XLM-R was pre-trained on Wikipedia and Common Crawl data from 100 different languages. A single model trained on 100 different languages.
* So, there is a single shared vocabulary with 250K tokens to cover all 100 languages.
* There is no special marker which suggests which language it is.
* It wasn't trained with parallel data (i.e. same sentence in multiple languages)
# Cross Lingual Transfer (Zero-shot transfer)
Say you are building a classifier which will classify Hindi comments toxic or non-toxic. But say you have a training dataset in English of 225 K labeled comments called "Wikipedia Toxic Comments". The Cross Lingual Transfer of XLM-R will let you fine-tune XLM-R on Wikipedia Toxic Comments dataset in English, and then apply it to Hindi comments. So, XLM-R is able to take it's task specific knowledge that it learned in English and apply it on Hindi, even though we never showed it any Hindi exmaples. It's concept of transfer learning applied from one language to another which is called Cross Lingual Transfer. The same could be done on any other low-resource languages
We will see that training XLM-R purely on ~400K English samples acutally yields better results than fine-tuning a monolingual Hindi model on a much smaller Hindi dataset. This is also referred to as Zero-Shot Learning or Cross-Lingual Transfer
# Our Approach -
We will use MNLI that provides us with a large number of English training examples to fine-tune XLM-R on the general task of NLI. XNLI will provide us with a small number of NLI test examples in different languages for us it will be Hindi. MNLI (Multi-genre NLI) has 392,000 training examples and 20,000 development examples and 20,000 test examples. On the other hand XNLI is a small subset of examples from MNLI dataset which have been human-translated to 14 different languages and yes Hindi is a part of it. For each language we have 5,000 test set sentence pairs and 2,500 development set sentence pairs
# Fine-tuning Hyperparameters -
learning rate : 5e-6 with linear learning rate scheduling over total steps
epochs = 2
batch_size = 32
GPU : Tesla T4
# Using this model
This model can be used using Huggingface Transformers. I have created a pipeline for a sentence pair classification. Hope it will be useful.
```python
!pip3 install transformers
import transformers
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("salesken/xlm-roberta-base-finetuned-mnli-cross-lingual-transfer")
model = AutoModelForSequenceClassification.from_pretrained("salesken/xlm-roberta-base-finetuned-mnli-cross-lingual-transfer")
device = torch.device('cuda')
print('GPU: ', torch.cuda.get_device_name(0))
model.to(device)
# Here I am giving the sentence pairs and also the ground truth values (1 : Similar, 0 : NotSimilar)
# we have purposely used Hindi-Hindi , Hindi-English, Transliterated Hindi-Engish, Kannada and Spanish and also code-switching of languages
# You can try your own pairs and languages
u_premise = ['क्या आपका बेटा गणित में रूचि रखता है',
'क्या आपका बेटा गणित में रूचि रखता है',
'क्या आपका बेटा गणित में रूचि रखता है',
'मैं त्योहार के दौरान घर जा रहा हूँ',
'क्या आपका बेटा गणित में रूचि रखता है',
'hum ek cross lingual model banane ka soch rahe hai',
'we are planning to build a cross-lingual language model',
'Hi. A very good morning to all of you. Lets get started. ',
'शुभ प्रभात',
'how are you doing today sir ?',
'how are you doing today sir ?',
'Argentina and Brazil will play Copa America finals this weekend',
'I want to visit Spain and Portugal next year'
]
u_hypothesis = ['क्या आपकी बेटी को विज्ञान में दिलचस्पी है',
'is your daughther interested in science',
'is your son interested in stem subjects',
'इस साल मैं त्योहार के दौरान घर नहीं जाऊंगा',
'इस साल मैं त्योहार के दौरान घर नहीं जाऊंगा',
'हम एक क्रॉस-लिंगुअल लैंग्वेज मॉडल बनाने की योजना बना रहे हैं',
'hum ek क्रॉस-लिंगुअल model bana rahe hai',
'शुभ प्रभात',
'subh prabhat to all of you',
'ಇಂದು ನೀವು ಹೇಗಿದ್ದೀರಿ ಸರ್?',
'ನನ್ನ ಸ್ನಾತಕೋತ್ತರರಿಗಾಗಿ ನಾನು ಯಂತ್ರ ಕಲಿಕೆ ಮತ್ತು ಡೇಟಾ ವಿಜ್ಞಾನವನ್ನು ಮಾಡುತ್ತಿದ್ದೇನೆ',
'Argentina y Brasil jugarán la final de la Copa América este fin de semana',
'Puedes ver la aurora boreal desde Noruega']
ground_truth = [1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0]
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import textwrap
import pandas as pd
labels_ar = []
input_ids_ar = []
attn_masks_ar = []
max_len = 128
batch_size = 32
label_names = ['entailment', 'neutral', 'contradiction']
for u_prem, u_hyp in zip(u_premise, u_hypothesis):
encoded_dict = tokenizer.encode_plus(u_prem, u_hyp,
max_length=max_len,
padding='max_length',
truncation=True,
return_tensors='pt')
# Add this example to our lists.
input_ids_ar.append(encoded_dict['input_ids'])
attn_masks_ar.append(encoded_dict['attention_mask'])
# Convert each Python list of Tensors into a 2D Tensor matrix.
input_ids_ar = torch.cat(input_ids_ar, dim=0)
attn_masks_ar = torch.cat(attn_masks_ar, dim=0)
# Construct a TensorDataset from the encoded examples.
prediction_dataset = TensorDataset(input_ids_ar, attn_masks_ar)
# And a dataloader for handling batching.
prediction_dataloader = DataLoader(prediction_dataset, batch_size=batch_size)
# Put model in evaluation mode
model.eval()
# Tracking variables
predictions , true_labels = [], []
count = 0
# Predict
for batch in prediction_dataloader:
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask = batch
with torch.no_grad():
# Forward pass, calculate logit predictions
outputs = model(b_input_ids,
attention_mask=b_input_mask)
logits = outputs[0]
# Move logits to CPU
logits = logits.detach().cpu().numpy()
# Store predictions and true labels
predictions.append(logits)
# Combine the results across all batches.
flat_predictions = np.concatenate(predictions, axis=0)
# For each sample, pick the label (0, 1, or 2) with the highest score.
predicted_labels = np.argmax(flat_predictions, axis=1).flatten()
wrapper = textwrap.TextWrapper(width = 80, initial_indent=' ',subsequent_indent=' ')
def softmax(logits):
return np.exp(logits) / np.sum(np.exp(logits))
finalOut = []
for sentA, sentB, pred_logits, grnd_truth in zip(u_premise, u_hypothesis, flat_predictions, ground_truth):
probScore = softmax(pred_logits)
maxScore = {'idx': 0, 'score': 0}
for idx, score in enumerate(probScore):
if score > maxScore['score']:
maxScore['score'] = score
maxScore['idx'] = idx
if maxScore['score'] > 0.4:
if maxScore['idx'] == 0:
label = 'Similar'
else:
label = 'NotSimilar'
finalOut.append((sentA, sentB, label, grnd_truth, maxScore['score']))
pd.set_option("display.max_colwidth", -1)
pd.DataFrame(finalOut, columns = ['sentence-1', 'sentence-2', 'semantic_matching', 'ground_truth', 'probability_score'])
```
| 7,994 |
Jeevesh8/goog_bert_ft_cola-26 | null | Entry not found | 15 |
assemblyai/bert-large-uncased-sst2 | null | # BERT-Large-Uncased for Sentiment Analysis
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sentiment Treebank v2 (SST2)](https://nlp.stanford.edu/sentiment/); part of the [General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com) benchmark. This model was fine-tuned by the team at [AssemblyAI](https://www.assemblyai.com) and is released with the [corresponding blog post]().
## Usage
To download and utilize this model for sentiment analysis please execute the following:
```python
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("assemblyai/bert-large-uncased-sst2")
model = AutoModelForSequenceClassification.from_pretrained("assemblyai/bert-large-uncased-sst2")
tokenized_segments = tokenizer(["AssemblyAI is the best speech-to-text API for modern developers with performance being second to none!"], return_tensors="pt", padding=True, truncation=True)
tokenized_segments_input_ids, tokenized_segments_attention_mask = tokenized_segments.input_ids, tokenized_segments.attention_mask
model_predictions = F.softmax(model(input_ids=tokenized_segments_input_ids, attention_mask=tokenized_segments_attention_mask)['logits'], dim=1)
print("Positive probability: "+str(model_predictions[0][1].item()*100)+"%")
print("Negative probability: "+str(model_predictions[0][0].item()*100)+"%")
```
For questions about how to use this model feel free to contact the team at [AssemblyAI](https://www.assemblyai.com)! | 1,758 |
Jeevesh8/goog_bert_ft_cola-27 | null | Entry not found | 15 |
ishan/bert-base-uncased-mnli | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | ---
language: en
thumbnail:
tags:
- pytorch
- text-classification
datasets:
- MNLI
---
# bert-base-uncased finetuned on MNLI
## Model Details and Training Data
We used the pretrained model from [bert-base-uncased](https://huggingface.co/bert-base-uncased) and finetuned it on [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset.
The training parameters were kept the same as [Devlin et al., 2019](https://arxiv.org/abs/1810.04805) (learning rate = 2e-5, training epochs = 3, max_sequence_len = 128 and batch_size = 32).
## Evaluation Results
The evaluation results are mentioned in the table below.
| Test Corpus | Accuracy |
|:---:|:---------:|
| Matched | 0.8456 |
| Mismatched | 0.8484 |
| 709 |
Jeevesh8/goog_bert_ft_cola-28 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-29 | null | Entry not found | 15 |
fabriceyhc/bert-base-uncased-imdb | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: bert-base-uncased-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.91264
---
<!-- 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-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4942
- Accuracy: 0.9126
## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1546
- training_steps: 15468
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3952 | 0.65 | 2000 | 0.4012 | 0.86 |
| 0.2954 | 1.29 | 4000 | 0.4535 | 0.892 |
| 0.2595 | 1.94 | 6000 | 0.4320 | 0.892 |
| 0.1516 | 2.59 | 8000 | 0.5309 | 0.896 |
| 0.1167 | 3.23 | 10000 | 0.4070 | 0.928 |
| 0.0624 | 3.88 | 12000 | 0.5055 | 0.908 |
| 0.0329 | 4.52 | 14000 | 0.4342 | 0.92 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
| 1,993 |
digitalepidemiologylab/covid-twitter-bert-v2-mnli | [
"contradiction",
"neutral",
"entailment"
] | ---
language:
- en
thumbnail: https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png
tags:
- Twitter
- COVID-19
- text-classification
- pytorch
- tensorflow
- bert
license: mit
datasets:
- mnli
pipeline_tag: zero-shot-classification
widget:
- text: To stop the pandemic it is important that everyone turns up for their shots.
candidate_labels: health, sport, vaccine, guns
---
# COVID-Twitter-BERT v2 MNLI
## Model description
This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.
The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161).
The article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.
The model is already finetuned on 400'000 generaic logical tasks.
We can then use it as a zero-shot classifier by reformulating the classification task as a question.
Let's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.
The typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.
Then you would finetune the model on this.
With the zero-shot mnli-classifier, you can instead reformulate your question as "This text is about vaccines", and use this directly on inference - without any training.
Find more info about the model on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
## Usage
Please note that how you formulate the question can give slightly different results.
Collecting a training set and finetuning on this, will most likely give you better accuracy.
The easiest way to try this out is by using the Hugging Face pipeline.
This uses the default Enlish template where it puts the text "This example is " in front of the text.
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="digitalepidemiologylab/covid-twitter-bert-v2-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = 'To stop the pandemic it is important that everyone turns up for their shots.'
candidate_labels = ['health', 'sport', 'vaccine','guns']
hypothesis_template = 'This example is {}.'
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
```
## Training procedure
The model is finetuned on the 400k large [MNLI-task](https://cims.nyu.edu/~sbowman/multinli/).
## References
```bibtex
@article{muller2020covid,
title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter},
author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E},
journal={arXiv preprint arXiv:2005.07503},
year={2020}
}
```
or
```
Martin Müller, Marcel Salathé, and Per E. Kummervold.
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter.
arXiv preprint arXiv:2005.07503 (2020).
```
| 3,085 |
Jeevesh8/goog_bert_ft_cola-30 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-31 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-32 | null | Entry not found | 15 |
Skoltech/russian-inappropriate-messages | null | ---
language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## General concept of the model
#### Proposed usage
The **'inappropriateness'** substance we tried to collect in the dataset and detect with the model **is NOT a substitution of toxicity**, it is rather a derivative of toxicity. So the model based on our dataset could serve as **an additional layer of inappropriateness filtering after toxicity and obscenity filtration**. You can detect the exact sensitive topic by using [another model](https://huggingface.co/Skoltech/russian-sensitive-topics). The proposed pipeline is shown in the scheme below.
You can also train one classifier for both toxicity and inappropriateness detection. The data to be mixed with toxic labelled samples could be found on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/appropriateness/Appropriateness.csv) or on [kaggle](https://www.kaggle.com/nigula/russianinappropriatemessages)
#### Inappropraiteness intuition
This model is trained on the dataset of inappropriate messages of the Russian language. Generally, an inappropriate utterance is an utterance that has not obscene words or any kind of toxic intent, but can still harm the reputation of the speaker. Find some sample for more intuition in the table below. Learn more about the concept of inappropriateness [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/appropriateness/Appropriateness.csv) or on [kaggle](https://www.kaggle.com/nigula/russianinappropriatemessages). The properties of the dataset are the same as the one described in the article, the only difference is the size.
| Text | Inappropriate | Comment | Unsafe topic |
|------------------------------------------------------------------------------------------------------|---------------|----------------------------------------------------------------|---------------|
| Ладно бы видного деятеля завалили а тут какого то ноунейм нигру преступника | yes | Оправдание убийства | offline crime |
| Это нарушение УКРФ! | no | Осуждение нарушения закона | offline crime |
| Религия - это насилие высшего уровня. Верующего принуждают к вере еще в младенчестве. | yes | Оскорбление чувств верующих | religion |
| Христиане, как вы относитесь к иноверцам, в частности к Гамалиилу и к уверовавшим в Иисуса фарисеям? | no | Обычные вопросы к верующим без намека на оскорбление их чувств | religion |
| Хорошо что проституция запрещена | no | Осуждение простуитуции | prostitution |
| Возьму бабки и сниму шлюх на выходных | yes | Популяризация проституции | prostitution |
#### Metrics
The model was trained, validated, and tested only on the samples with 100% confidence, which allowed to get the following metrics on test set:
| | precision | recall | f1-score | support |
|--------------|----------|--------|----------|---------|
| 0 | 0.92 | 0.93 | 0.93 | 7839 |
| 1 | 0.80 | 0.76 | 0.78 | 2726 |
| accuracy | | | 0.89 | 10565 |
| macro avg | 0.86 | 0.85 | 0.85 | 10565 |
| weighted avg | 0.89 | 0.89 | 0.89 | 10565 |
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@inproceedings{babakov-etal-2021-detecting,
title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
author = "Babakov, Nikolay and
Logacheva, Varvara and
Kozlova, Olga and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}
```
## Contacts
If you have any questions please contact [Nikolay](mailto:N.Babakov@skoltech.ru) | 6,383 |
bhadresh-savani/distilbert-base-uncased-go-emotion | [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"neutral",
"optimism",
"pride",
"realization",
"relief",
"remorse",
"sadness",
"surprise"
] | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- go-emotion
- pytorch
license: apache-2.0
datasets:
- go_emotions
metrics:
- Accuracy
---
# Distilbert-Base-Uncased-Go-Emotion
## Model description:
**Not working fine**
## Training Parameters:
```
Num Epochs = 3
Instantaneous batch size per device = 32
Total train batch size (w. parallel, distributed & accumulation) = 32
Gradient Accumulation steps = 1
Total optimization steps = 15831
```
## TrainOutput:
```
'train_loss': 0.105500
```
## Evalution Output:
```
'eval_accuracy_thresh': 0.962023913860321,
'eval_loss': 0.11090277135372162,
```
## Colab Notebook:
[Notebook](https://github.com/bhadreshpsavani/UnderstandingNLP/blob/master/go_emotion_of_transformers_multilabel_text_classification_v2.ipynb) | 888 |
Jeevesh8/goog_bert_ft_cola-33 | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-34 | null | Entry not found | 15 |
cointegrated/roberta-base-formality | null | Entry not found | 15 |
Jeevesh8/goog_bert_ft_cola-35 | null | Entry not found | 15 |
sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english | [
"NEGATIVE",
"POSITIVE"
] | Entry not found | 15 |
responsibility-framing/predict-perception-bert-blame-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-object
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. -->
# predict-perception-bert-blame-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5837
- Rmse: 0.5589
- Rmse Blame::a Un oggetto: 0.5589
- Mae: 0.3862
- Mae Blame::a Un oggetto: 0.3862
- R2: 0.2884
- R2 Blame::a Un oggetto: 0.2884
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5024
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un oggetto | Mae | Mae Blame::a Un oggetto | R2 | R2 Blame::a Un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0603 | 1.0 | 15 | 0.8503 | 0.6745 | 0.6745 | 0.4386 | 0.4386 | -0.0365 | -0.0365 | 0.1304 | 0.0 | 0.5 | 0.5197 | nan |
| 0.9662 | 2.0 | 30 | 0.8510 | 0.6748 | 0.6748 | 0.4548 | 0.4548 | -0.0374 | -0.0374 | 0.0435 | 0.0 | 0.5 | 0.4840 | nan |
| 0.9438 | 3.0 | 45 | 0.7622 | 0.6386 | 0.6386 | 0.4541 | 0.4541 | 0.0709 | 0.0709 | 0.0435 | 0.0 | 0.5 | 0.4635 | nan |
| 0.9096 | 4.0 | 60 | 0.8301 | 0.6665 | 0.6665 | 0.4305 | 0.4305 | -0.0119 | -0.0119 | 0.0435 | 0.0 | 0.5 | 0.3499 | nan |
| 0.8383 | 5.0 | 75 | 0.7306 | 0.6252 | 0.6252 | 0.3814 | 0.3814 | 0.1094 | 0.1094 | 0.3043 | 0.0 | 0.5 | 0.5098 | nan |
| 0.7828 | 6.0 | 90 | 0.7434 | 0.6307 | 0.6307 | 0.4005 | 0.4005 | 0.0937 | 0.0937 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.7028 | 7.0 | 105 | 0.7218 | 0.6214 | 0.6214 | 0.4090 | 0.4090 | 0.1202 | 0.1202 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.6661 | 8.0 | 120 | 0.7434 | 0.6307 | 0.6307 | 0.4042 | 0.4042 | 0.0938 | 0.0938 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.578 | 9.0 | 135 | 0.7719 | 0.6426 | 0.6426 | 0.3975 | 0.3975 | 0.0591 | 0.0591 | 0.3913 | 0.0 | 0.5 | 0.4470 | nan |
| 0.544 | 10.0 | 150 | 0.7117 | 0.6171 | 0.6171 | 0.4126 | 0.4126 | 0.1324 | 0.1324 | 0.2174 | 0.0 | 0.5 | 0.3489 | nan |
| 0.4638 | 11.0 | 165 | 0.6683 | 0.5980 | 0.5980 | 0.3952 | 0.3952 | 0.1853 | 0.1853 | 0.3043 | 0.0 | 0.5 | 0.3989 | nan |
| 0.3998 | 12.0 | 180 | 0.6772 | 0.6019 | 0.6019 | 0.4201 | 0.4201 | 0.1745 | 0.1745 | 0.3043 | 0.0 | 0.5 | 0.3989 | nan |
| 0.3403 | 13.0 | 195 | 0.6576 | 0.5932 | 0.5932 | 0.4237 | 0.4237 | 0.1984 | 0.1984 | 0.2174 | 0.0 | 0.5 | 0.3491 | nan |
| 0.2839 | 14.0 | 210 | 0.6281 | 0.5797 | 0.5797 | 0.4208 | 0.4208 | 0.2344 | 0.2344 | 0.2174 | 0.0 | 0.5 | 0.3491 | nan |
| 0.2619 | 15.0 | 225 | 0.6254 | 0.5785 | 0.5785 | 0.3752 | 0.3752 | 0.2376 | 0.2376 | 0.3913 | 0.0 | 0.5 | 0.5756 | nan |
| 0.2175 | 16.0 | 240 | 0.6074 | 0.5701 | 0.5701 | 0.3985 | 0.3985 | 0.2596 | 0.2596 | 0.3043 | 0.0 | 0.5 | 0.4142 | nan |
| 0.1884 | 17.0 | 255 | 0.6045 | 0.5687 | 0.5687 | 0.4036 | 0.4036 | 0.2631 | 0.2631 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1797 | 18.0 | 270 | 0.6038 | 0.5684 | 0.5684 | 0.3914 | 0.3914 | 0.2640 | 0.2640 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1316 | 19.0 | 285 | 0.6199 | 0.5759 | 0.5759 | 0.4078 | 0.4078 | 0.2443 | 0.2443 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1429 | 20.0 | 300 | 0.6119 | 0.5722 | 0.5722 | 0.3954 | 0.3954 | 0.2540 | 0.2540 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1202 | 21.0 | 315 | 0.6193 | 0.5756 | 0.5756 | 0.3987 | 0.3987 | 0.2451 | 0.2451 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1159 | 22.0 | 330 | 0.6218 | 0.5768 | 0.5768 | 0.3995 | 0.3995 | 0.2420 | 0.2420 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.1027 | 23.0 | 345 | 0.6207 | 0.5763 | 0.5763 | 0.4100 | 0.4100 | 0.2433 | 0.2433 | 0.3043 | 0.0 | 0.5 | 0.4142 | nan |
| 0.1006 | 24.0 | 360 | 0.5646 | 0.5496 | 0.5496 | 0.3687 | 0.3687 | 0.3117 | 0.3117 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0902 | 25.0 | 375 | 0.5582 | 0.5465 | 0.5465 | 0.3714 | 0.3714 | 0.3196 | 0.3196 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0901 | 26.0 | 390 | 0.5650 | 0.5498 | 0.5498 | 0.3704 | 0.3704 | 0.3112 | 0.3112 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0937 | 27.0 | 405 | 0.5713 | 0.5529 | 0.5529 | 0.3735 | 0.3735 | 0.3036 | 0.3036 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0812 | 28.0 | 420 | 0.5773 | 0.5558 | 0.5558 | 0.3759 | 0.3759 | 0.2962 | 0.2962 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.0911 | 29.0 | 435 | 0.5818 | 0.5579 | 0.5579 | 0.3832 | 0.3832 | 0.2908 | 0.2908 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
| 0.082 | 30.0 | 450 | 0.5837 | 0.5589 | 0.5589 | 0.3862 | 0.3862 | 0.2884 | 0.2884 | 0.3913 | 0.0 | 0.5 | 0.5024 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,718 |
responsibility-framing/predict-perception-bert-cause-human | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-human
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. -->
# predict-perception-bert-cause-human
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7139
- Rmse: 1.2259
- Rmse Cause::a Causata da un essere umano: 1.2259
- Mae: 1.0480
- Mae Cause::a Causata da un essere umano: 1.0480
- R2: 0.4563
- R2 Cause::a Causata da un essere umano: 0.4563
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3953
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un essere umano | Mae | Mae Cause::a Causata da un essere umano | R2 | R2 Cause::a Causata da un essere umano | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:------:|:--------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0874 | 1.0 | 15 | 1.2615 | 1.6296 | 1.6296 | 1.3836 | 1.3836 | 0.0393 | 0.0393 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan |
| 0.9577 | 2.0 | 30 | 1.1988 | 1.5886 | 1.5886 | 1.3017 | 1.3017 | 0.0870 | 0.0870 | 0.4783 | 0.0 | 0.5 | 0.3944 | nan |
| 0.8414 | 3.0 | 45 | 0.9870 | 1.4414 | 1.4414 | 1.1963 | 1.1963 | 0.2483 | 0.2483 | 0.3913 | 0.0 | 0.5 | 0.3048 | nan |
| 0.7291 | 4.0 | 60 | 0.9098 | 1.3839 | 1.3839 | 1.1297 | 1.1297 | 0.3071 | 0.3071 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.5949 | 5.0 | 75 | 0.9207 | 1.3921 | 1.3921 | 1.2079 | 1.2079 | 0.2988 | 0.2988 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.4938 | 6.0 | 90 | 0.8591 | 1.3448 | 1.3448 | 1.1842 | 1.1842 | 0.3458 | 0.3458 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.3611 | 7.0 | 105 | 0.8176 | 1.3119 | 1.3119 | 1.1454 | 1.1454 | 0.3774 | 0.3774 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.2663 | 8.0 | 120 | 0.6879 | 1.2034 | 1.2034 | 1.0300 | 1.0300 | 0.4761 | 0.4761 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.1833 | 9.0 | 135 | 0.7704 | 1.2735 | 1.2735 | 1.1031 | 1.1031 | 0.4133 | 0.4133 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.1704 | 10.0 | 150 | 0.7097 | 1.2222 | 1.2222 | 1.0382 | 1.0382 | 0.4596 | 0.4596 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.1219 | 11.0 | 165 | 0.6872 | 1.2027 | 1.2027 | 1.0198 | 1.0198 | 0.4767 | 0.4767 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.1011 | 12.0 | 180 | 0.7201 | 1.2312 | 1.2312 | 1.0466 | 1.0466 | 0.4516 | 0.4516 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.0849 | 13.0 | 195 | 0.7267 | 1.2368 | 1.2368 | 1.0454 | 1.0454 | 0.4466 | 0.4466 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0818 | 14.0 | 210 | 0.7361 | 1.2448 | 1.2448 | 1.0565 | 1.0565 | 0.4394 | 0.4394 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0634 | 15.0 | 225 | 0.7158 | 1.2275 | 1.2275 | 1.0384 | 1.0384 | 0.4549 | 0.4549 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.065 | 16.0 | 240 | 0.7394 | 1.2475 | 1.2475 | 1.0659 | 1.0659 | 0.4369 | 0.4369 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0541 | 17.0 | 255 | 0.7642 | 1.2683 | 1.2683 | 1.0496 | 1.0496 | 0.4181 | 0.4181 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0577 | 18.0 | 270 | 0.7137 | 1.2257 | 1.2257 | 1.0303 | 1.0303 | 0.4565 | 0.4565 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0474 | 19.0 | 285 | 0.7393 | 1.2475 | 1.2475 | 1.0447 | 1.0447 | 0.4370 | 0.4370 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0494 | 20.0 | 300 | 0.7157 | 1.2274 | 1.2274 | 1.0453 | 1.0453 | 0.4550 | 0.4550 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0434 | 21.0 | 315 | 0.7248 | 1.2352 | 1.2352 | 1.0462 | 1.0462 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.049 | 22.0 | 330 | 0.7384 | 1.2467 | 1.2467 | 1.0613 | 1.0613 | 0.4377 | 0.4377 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0405 | 23.0 | 345 | 0.7420 | 1.2498 | 1.2498 | 1.0653 | 1.0653 | 0.4349 | 0.4349 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0398 | 24.0 | 360 | 0.7355 | 1.2442 | 1.2442 | 1.0620 | 1.0620 | 0.4399 | 0.4399 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0398 | 25.0 | 375 | 0.7570 | 1.2623 | 1.2623 | 1.0698 | 1.0698 | 0.4235 | 0.4235 | 0.3913 | 0.0 | 0.5 | 0.3306 | nan |
| 0.0345 | 26.0 | 390 | 0.7359 | 1.2446 | 1.2446 | 1.0610 | 1.0610 | 0.4396 | 0.4396 | 0.5652 | 0.0 | 0.5 | 0.3152 | nan |
| 0.0345 | 27.0 | 405 | 0.7417 | 1.2495 | 1.2495 | 1.0660 | 1.0660 | 0.4352 | 0.4352 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
| 0.0386 | 28.0 | 420 | 0.7215 | 1.2323 | 1.2323 | 1.0514 | 1.0514 | 0.4506 | 0.4506 | 0.4783 | 0.0 | 0.5 | 0.3084 | nan |
| 0.0372 | 29.0 | 435 | 0.7140 | 1.2260 | 1.2260 | 1.0477 | 1.0477 | 0.4562 | 0.4562 | 0.5652 | 0.0 | 0.5 | 0.4091 | nan |
| 0.0407 | 30.0 | 450 | 0.7139 | 1.2259 | 1.2259 | 1.0480 | 1.0480 | 0.4563 | 0.4563 | 0.4783 | 0.0 | 0.5 | 0.3953 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 9,268 |
responsibility-framing/predict-perception-xlmr-cause-human | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-human
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. -->
# predict-perception-xlmr-cause-human
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7632
- Rmse: 1.2675
- Rmse Cause::a Causata da un essere umano: 1.2675
- Mae: 0.9299
- Mae Cause::a Causata da un essere umano: 0.9299
- R2: 0.4188
- R2 Cause::a Causata da un essere umano: 0.4188
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4082
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un essere umano | Mae | Mae Cause::a Causata da un essere umano | R2 | R2 Cause::a Causata da un essere umano | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------------------:|:------:|:---------------------------------------:|:-------:|:--------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0174 | 1.0 | 15 | 1.3796 | 1.7041 | 1.7041 | 1.3614 | 1.3614 | -0.0506 | -0.0506 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 0.9534 | 2.0 | 30 | 1.1173 | 1.5336 | 1.5336 | 1.2624 | 1.2624 | 0.1491 | 0.1491 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.8883 | 3.0 | 45 | 1.0580 | 1.4923 | 1.4923 | 1.2451 | 1.2451 | 0.1943 | 0.1943 | 0.5652 | 0.0 | 0.5 | 0.4957 | nan |
| 0.8215 | 4.0 | 60 | 1.0200 | 1.4653 | 1.4653 | 1.2087 | 1.2087 | 0.2232 | 0.2232 | 0.6522 | 0.0 | 0.5 | 0.5123 | nan |
| 0.744 | 5.0 | 75 | 1.1496 | 1.5556 | 1.5556 | 1.2573 | 1.2573 | 0.1245 | 0.1245 | 0.2174 | 0.0 | 0.5 | 0.3007 | nan |
| 0.7056 | 6.0 | 90 | 0.9641 | 1.4246 | 1.4246 | 1.1763 | 1.1763 | 0.2658 | 0.2658 | 0.4783 | 0.0 | 0.5 | 0.3619 | nan |
| 0.6136 | 7.0 | 105 | 0.8328 | 1.3240 | 1.3240 | 1.0948 | 1.0948 | 0.3658 | 0.3658 | 0.4783 | 0.0 | 0.5 | 0.3628 | nan |
| 0.5185 | 8.0 | 120 | 0.6890 | 1.2043 | 1.2043 | 1.0112 | 1.0112 | 0.4753 | 0.4753 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.5029 | 9.0 | 135 | 1.0380 | 1.4782 | 1.4782 | 1.1215 | 1.1215 | 0.2095 | 0.2095 | 0.3913 | 0.0 | 0.5 | 0.3781 | nan |
| 0.4624 | 10.0 | 150 | 1.1780 | 1.5747 | 1.5747 | 1.2852 | 1.2852 | 0.1029 | 0.1029 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.4098 | 11.0 | 165 | 0.8714 | 1.3544 | 1.3544 | 1.1388 | 1.1388 | 0.3364 | 0.3364 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.348 | 12.0 | 180 | 0.7260 | 1.2362 | 1.2362 | 0.9563 | 0.9563 | 0.4471 | 0.4471 | 0.5652 | 0.0 | 0.5 | 0.4957 | nan |
| 0.3437 | 13.0 | 195 | 0.7241 | 1.2346 | 1.2346 | 0.8998 | 0.8998 | 0.4485 | 0.4485 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan |
| 0.2727 | 14.0 | 210 | 0.9070 | 1.3818 | 1.3818 | 1.1145 | 1.1145 | 0.3093 | 0.3093 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.2762 | 15.0 | 225 | 0.7280 | 1.2380 | 1.2380 | 0.9210 | 0.9210 | 0.4456 | 0.4456 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2396 | 16.0 | 240 | 0.7921 | 1.2912 | 1.2912 | 0.9738 | 0.9738 | 0.3968 | 0.3968 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1955 | 17.0 | 255 | 0.8368 | 1.3272 | 1.3272 | 0.9717 | 0.9717 | 0.3627 | 0.3627 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1928 | 18.0 | 270 | 0.7782 | 1.2799 | 1.2799 | 0.9615 | 0.9615 | 0.4073 | 0.4073 | 0.3043 | 0.0 | 0.5 | 0.3768 | nan |
| 0.1893 | 19.0 | 285 | 0.7594 | 1.2644 | 1.2644 | 0.9441 | 0.9441 | 0.4216 | 0.4216 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2111 | 20.0 | 300 | 0.7230 | 1.2336 | 1.2336 | 0.8953 | 0.8953 | 0.4494 | 0.4494 | 0.3913 | 0.0 | 0.5 | 0.3787 | nan |
| 0.193 | 21.0 | 315 | 0.7836 | 1.2843 | 1.2843 | 0.9577 | 0.9577 | 0.4033 | 0.4033 | 0.3043 | 0.0 | 0.5 | 0.3768 | nan |
| 0.1649 | 22.0 | 330 | 0.7248 | 1.2352 | 1.2352 | 0.9133 | 0.9133 | 0.4480 | 0.4480 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.2182 | 23.0 | 345 | 0.7608 | 1.2655 | 1.2655 | 0.9435 | 0.9435 | 0.4206 | 0.4206 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1534 | 24.0 | 360 | 0.7447 | 1.2520 | 1.2520 | 0.9277 | 0.9277 | 0.4329 | 0.4329 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1362 | 25.0 | 375 | 0.7437 | 1.2512 | 1.2512 | 0.9236 | 0.9236 | 0.4336 | 0.4336 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1391 | 26.0 | 390 | 0.7301 | 1.2397 | 1.2397 | 0.9182 | 0.9182 | 0.4440 | 0.4440 | 0.4783 | 0.0 | 0.5 | 0.4446 | nan |
| 0.1679 | 27.0 | 405 | 0.7748 | 1.2770 | 1.2770 | 0.9619 | 0.9619 | 0.4100 | 0.4100 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1491 | 28.0 | 420 | 0.7415 | 1.2493 | 1.2493 | 0.9097 | 0.9097 | 0.4353 | 0.4353 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1559 | 29.0 | 435 | 0.7525 | 1.2586 | 1.2586 | 0.9189 | 0.9189 | 0.4269 | 0.4269 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
| 0.1784 | 30.0 | 450 | 0.7632 | 1.2675 | 1.2675 | 0.9299 | 0.9299 | 0.4188 | 0.4188 | 0.3913 | 0.0 | 0.5 | 0.4082 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 9,298 |
Jeevesh8/goog_bert_ft_cola-36 | null | Entry not found | 15 |
responsibility-framing/predict-perception-bert-focus-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-concept
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. -->
# predict-perception-bert-focus-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8129
- Rmse: 1.0197
- Rmse Focus::a Su un concetto astratto o un'emozione: 1.0197
- Mae: 0.7494
- Mae Focus::a Su un concetto astratto o un'emozione: 0.7494
- R2: 0.1970
- R2 Focus::a Su un concetto astratto o un'emozione: 0.1970
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4667
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un concetto astratto o un'emozione | Mae | Mae Focus::a Su un concetto astratto o un'emozione | R2 | R2 Focus::a Su un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------:|:------:|:--------------------------------------------------:|:-------:|:-------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.047 | 1.0 | 15 | 1.0199 | 1.1422 | 1.1422 | 0.9321 | 0.9321 | -0.0075 | -0.0075 | 0.1304 | 0.0 | 0.5 | 0.3199 | nan |
| 0.9914 | 2.0 | 30 | 0.9724 | 1.1153 | 1.1153 | 0.9407 | 0.9407 | 0.0393 | 0.0393 | 0.2174 | 0.0 | 0.5 | 0.3954 | nan |
| 0.9049 | 3.0 | 45 | 0.9406 | 1.0969 | 1.0969 | 0.9170 | 0.9170 | 0.0708 | 0.0708 | 0.2174 | 0.0 | 0.5 | 0.3632 | nan |
| 0.8826 | 4.0 | 60 | 0.8553 | 1.0460 | 1.0460 | 0.8570 | 0.8570 | 0.1551 | 0.1551 | 0.2174 | 0.0 | 0.5 | 0.3230 | nan |
| 0.7837 | 5.0 | 75 | 0.8324 | 1.0319 | 1.0319 | 0.8683 | 0.8683 | 0.1776 | 0.1776 | 0.2174 | 0.0 | 0.5 | 0.3419 | nan |
| 0.7013 | 6.0 | 90 | 0.7737 | 0.9949 | 0.9949 | 0.8150 | 0.8150 | 0.2356 | 0.2356 | 0.5652 | 0.0 | 0.5 | 0.5023 | nan |
| 0.6429 | 7.0 | 105 | 0.7832 | 1.0010 | 1.0010 | 0.8005 | 0.8005 | 0.2262 | 0.2262 | 0.3913 | 0.0 | 0.5 | 0.4446 | nan |
| 0.5526 | 8.0 | 120 | 0.7734 | 0.9946 | 0.9946 | 0.7704 | 0.7704 | 0.2360 | 0.2360 | 0.3043 | 0.0 | 0.5 | 0.2923 | nan |
| 0.5194 | 9.0 | 135 | 0.6624 | 0.9205 | 0.9205 | 0.7013 | 0.7013 | 0.3456 | 0.3456 | 0.3913 | 0.0 | 0.5 | 0.3523 | nan |
| 0.4278 | 10.0 | 150 | 0.8255 | 1.0276 | 1.0276 | 0.7351 | 0.7351 | 0.1845 | 0.1845 | 0.3043 | 0.0 | 0.5 | 0.4349 | nan |
| 0.3522 | 11.0 | 165 | 0.9340 | 1.0931 | 1.0931 | 0.8069 | 0.8069 | 0.0773 | 0.0773 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.314 | 12.0 | 180 | 0.7495 | 0.9792 | 0.9792 | 0.7254 | 0.7254 | 0.2596 | 0.2596 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.2665 | 13.0 | 195 | 0.8574 | 1.0473 | 1.0473 | 0.7678 | 0.7678 | 0.1530 | 0.1530 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.2348 | 14.0 | 210 | 0.7913 | 1.0061 | 1.0061 | 0.7218 | 0.7218 | 0.2183 | 0.2183 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.1859 | 15.0 | 225 | 0.8012 | 1.0124 | 1.0124 | 0.7162 | 0.7162 | 0.2085 | 0.2085 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.1373 | 16.0 | 240 | 0.8405 | 1.0369 | 1.0369 | 0.7318 | 0.7318 | 0.1697 | 0.1697 | 0.3043 | 0.0 | 0.5 | 0.3734 | nan |
| 0.1245 | 17.0 | 255 | 0.8398 | 1.0365 | 1.0365 | 0.7455 | 0.7455 | 0.1703 | 0.1703 | 0.4783 | 0.0 | 0.5 | 0.4667 | nan |
| 0.1148 | 18.0 | 270 | 0.7948 | 1.0083 | 1.0083 | 0.7140 | 0.7140 | 0.2148 | 0.2148 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.1187 | 19.0 | 285 | 0.8301 | 1.0305 | 1.0305 | 0.7381 | 0.7381 | 0.1799 | 0.1799 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.1236 | 20.0 | 300 | 0.8867 | 1.0650 | 1.0650 | 0.7879 | 0.7879 | 0.1240 | 0.1240 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.1101 | 21.0 | 315 | 0.8405 | 1.0369 | 1.0369 | 0.7632 | 0.7632 | 0.1696 | 0.1696 | 0.3913 | 0.0 | 0.5 | 0.4059 | nan |
| 0.0902 | 22.0 | 330 | 0.7850 | 1.0021 | 1.0021 | 0.7173 | 0.7173 | 0.2245 | 0.2245 | 0.3043 | 0.0 | 0.5 | 0.3734 | nan |
| 0.093 | 23.0 | 345 | 0.7386 | 0.9720 | 0.9720 | 0.6960 | 0.6960 | 0.2704 | 0.2704 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.0846 | 24.0 | 360 | 0.7748 | 0.9956 | 0.9956 | 0.7150 | 0.7150 | 0.2345 | 0.2345 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.0826 | 25.0 | 375 | 0.7951 | 1.0085 | 1.0085 | 0.7230 | 0.7230 | 0.2145 | 0.2145 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.0749 | 26.0 | 390 | 0.8470 | 1.0409 | 1.0409 | 0.7621 | 0.7621 | 0.1633 | 0.1633 | 0.4783 | 0.0 | 0.5 | 0.4667 | nan |
| 0.069 | 27.0 | 405 | 0.7968 | 1.0096 | 1.0096 | 0.7275 | 0.7275 | 0.2129 | 0.2129 | 0.3913 | 0.0 | 0.5 | 0.4175 | nan |
| 0.0775 | 28.0 | 420 | 0.8298 | 1.0303 | 1.0303 | 0.7589 | 0.7589 | 0.1802 | 0.1802 | 0.4783 | 0.0 | 0.5 | 0.4667 | nan |
| 0.0783 | 29.0 | 435 | 0.8113 | 1.0188 | 1.0188 | 0.7469 | 0.7469 | 0.1985 | 0.1985 | 0.4783 | 0.0 | 0.5 | 0.4667 | nan |
| 0.0773 | 30.0 | 450 | 0.8129 | 1.0197 | 1.0197 | 0.7494 | 0.7494 | 0.1970 | 0.1970 | 0.4783 | 0.0 | 0.5 | 0.4667 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,393 |
Jeevesh8/goog_bert_ft_cola-37 | null | Entry not found | 15 |
responsibility-framing/predict-perception-bert-cause-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-object
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. -->
# predict-perception-bert-cause-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4120
- Rmse: 1.0345
- Rmse Cause::a Causata da un oggetto (es. una pistola): 1.0345
- Mae: 0.6181
- Mae Cause::a Causata da un oggetto (es. una pistola): 0.6181
- R2: 0.3837
- R2 Cause::a Causata da un oggetto (es. una pistola): 0.3837
- Cos: 0.9130
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.8986
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un oggetto (es. una pistola) | Mae | Mae Cause::a Causata da un oggetto (es. una pistola) | R2 | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0824 | 1.0 | 15 | 0.6651 | 1.3143 | 1.3143 | 1.0930 | 1.0930 | 0.0052 | 0.0052 | 0.3043 | 0.0 | 0.5 | 0.4393 | nan |
| 0.9574 | 2.0 | 30 | 0.7088 | 1.3568 | 1.3568 | 1.1945 | 1.1945 | -0.0601 | -0.0601 | 0.0435 | 0.0 | 0.5 | 0.3380 | nan |
| 0.8151 | 3.0 | 45 | 0.6300 | 1.2791 | 1.2791 | 1.0206 | 1.0206 | 0.0577 | 0.0577 | 0.3043 | 0.0 | 0.5 | 0.3613 | nan |
| 0.6401 | 4.0 | 60 | 0.4871 | 1.1247 | 1.1247 | 0.7285 | 0.7285 | 0.2715 | 0.2715 | 0.5652 | 0.0 | 0.5 | 0.6424 | nan |
| 0.448 | 5.0 | 75 | 0.5005 | 1.1401 | 1.1401 | 0.7216 | 0.7216 | 0.2514 | 0.2514 | 0.4783 | 0.0 | 0.5 | 0.6077 | nan |
| 0.2893 | 6.0 | 90 | 0.4761 | 1.1119 | 1.1119 | 0.7237 | 0.7237 | 0.2879 | 0.2879 | 0.5652 | 0.0 | 0.5 | 0.6348 | nan |
| 0.174 | 7.0 | 105 | 0.4771 | 1.1131 | 1.1131 | 0.6836 | 0.6836 | 0.2865 | 0.2865 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan |
| 0.1383 | 8.0 | 120 | 0.4313 | 1.0583 | 1.0583 | 0.6462 | 0.6462 | 0.3550 | 0.3550 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.1105 | 9.0 | 135 | 0.4660 | 1.1001 | 1.1001 | 0.6737 | 0.6737 | 0.3030 | 0.3030 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0903 | 10.0 | 150 | 0.4866 | 1.1241 | 1.1241 | 0.7192 | 0.7192 | 0.2723 | 0.2723 | 0.7391 | 0.0 | 0.5 | 0.6833 | nan |
| 0.0571 | 11.0 | 165 | 0.4361 | 1.0642 | 1.0642 | 0.6130 | 0.6130 | 0.3478 | 0.3478 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0623 | 12.0 | 180 | 0.4578 | 1.0904 | 1.0904 | 0.6844 | 0.6844 | 0.3152 | 0.3152 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan |
| 0.0526 | 13.0 | 195 | 0.4605 | 1.0936 | 1.0936 | 0.6697 | 0.6697 | 0.3112 | 0.3112 | 0.6522 | 0.0 | 0.5 | 0.6785 | nan |
| 0.0472 | 14.0 | 210 | 0.4440 | 1.0738 | 1.0738 | 0.6589 | 0.6589 | 0.3360 | 0.3360 | 0.7391 | 0.0 | 0.5 | 0.7327 | nan |
| 0.0492 | 15.0 | 225 | 0.4593 | 1.0922 | 1.0922 | 0.6812 | 0.6812 | 0.3130 | 0.3130 | 0.7391 | 0.0 | 0.5 | 0.6833 | nan |
| 0.0389 | 16.0 | 240 | 0.4195 | 1.0437 | 1.0437 | 0.6252 | 0.6252 | 0.3726 | 0.3726 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0396 | 17.0 | 255 | 0.4087 | 1.0302 | 1.0302 | 0.6119 | 0.6119 | 0.3888 | 0.3888 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0328 | 18.0 | 270 | 0.4274 | 1.0535 | 1.0535 | 0.6457 | 0.6457 | 0.3608 | 0.3608 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan |
| 0.0345 | 19.0 | 285 | 0.4306 | 1.0574 | 1.0574 | 0.6576 | 0.6576 | 0.3560 | 0.3560 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan |
| 0.0328 | 20.0 | 300 | 0.4067 | 1.0277 | 1.0277 | 0.6160 | 0.6160 | 0.3918 | 0.3918 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0344 | 21.0 | 315 | 0.4056 | 1.0263 | 1.0263 | 0.5948 | 0.5948 | 0.3934 | 0.3934 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0312 | 22.0 | 330 | 0.4236 | 1.0488 | 1.0488 | 0.6277 | 0.6277 | 0.3665 | 0.3665 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0241 | 23.0 | 345 | 0.4272 | 1.0533 | 1.0533 | 0.6444 | 0.6444 | 0.3610 | 0.3610 | 0.8261 | 0.0 | 0.5 | 0.7431 | nan |
| 0.0302 | 24.0 | 360 | 0.4046 | 1.0250 | 1.0250 | 0.6030 | 0.6030 | 0.3949 | 0.3949 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0244 | 25.0 | 375 | 0.4194 | 1.0436 | 1.0436 | 0.6320 | 0.6320 | 0.3728 | 0.3728 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0259 | 26.0 | 390 | 0.4025 | 1.0224 | 1.0224 | 0.6009 | 0.6009 | 0.3980 | 0.3980 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0265 | 27.0 | 405 | 0.4103 | 1.0323 | 1.0323 | 0.6180 | 0.6180 | 0.3863 | 0.3863 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0184 | 28.0 | 420 | 0.4059 | 1.0268 | 1.0268 | 0.6046 | 0.6046 | 0.3929 | 0.3929 | 0.8261 | 0.0 | 0.5 | 0.7586 | nan |
| 0.0257 | 29.0 | 435 | 0.4088 | 1.0304 | 1.0304 | 0.6122 | 0.6122 | 0.3885 | 0.3885 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
| 0.0262 | 30.0 | 450 | 0.4120 | 1.0345 | 1.0345 | 0.6181 | 0.6181 | 0.3837 | 0.3837 | 0.9130 | 0.0 | 0.5 | 0.8986 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,589 |
dmis-lab/biobert-base-cased-v1.1-mnli | [
"LABEL_0",
"LABEL_1"
] | Entry not found | 15 |
responsibility-framing/predict-perception-bert-blame-none | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-none
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. -->
# predict-perception-bert-blame-none
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8646
- Rmse: 1.1072
- Rmse Blame::a Nessuno: 1.1072
- Mae: 0.8721
- Mae Blame::a Nessuno: 0.8721
- R2: 0.3083
- R2 Blame::a Nessuno: 0.3083
- Cos: 0.5652
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5070
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Nessuno | Mae | Mae Blame::a Nessuno | R2 | R2 Blame::a Nessuno | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.007 | 1.0 | 15 | 1.2585 | 1.3358 | 1.3358 | 1.1752 | 1.1752 | -0.0068 | -0.0068 | -0.0435 | 0.0 | 0.5 | 0.2970 | nan |
| 0.927 | 2.0 | 30 | 1.1310 | 1.2663 | 1.2663 | 1.0633 | 1.0633 | 0.0952 | 0.0952 | 0.4783 | 0.0 | 0.5 | 0.4012 | nan |
| 0.8376 | 3.0 | 45 | 1.0603 | 1.2261 | 1.2261 | 1.0574 | 1.0574 | 0.1518 | 0.1518 | 0.1304 | 0.0 | 0.5 | 0.2970 | nan |
| 0.7154 | 4.0 | 60 | 0.8347 | 1.0879 | 1.0879 | 0.8854 | 0.8854 | 0.3323 | 0.3323 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.5766 | 5.0 | 75 | 0.7426 | 1.0261 | 1.0261 | 0.8340 | 0.8340 | 0.4059 | 0.4059 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.4632 | 6.0 | 90 | 0.6671 | 0.9725 | 0.9725 | 0.7932 | 0.7932 | 0.4663 | 0.4663 | 0.6522 | 0.0 | 0.5 | 0.5209 | nan |
| 0.3854 | 7.0 | 105 | 0.6447 | 0.9561 | 0.9561 | 0.7424 | 0.7424 | 0.4842 | 0.4842 | 0.6522 | 0.0 | 0.5 | 0.4307 | nan |
| 0.3154 | 8.0 | 120 | 0.7198 | 1.0102 | 1.0102 | 0.8113 | 0.8113 | 0.4241 | 0.4241 | 0.6522 | 0.0 | 0.5 | 0.4307 | nan |
| 0.2637 | 9.0 | 135 | 0.7221 | 1.0118 | 1.0118 | 0.8319 | 0.8319 | 0.4223 | 0.4223 | 0.5652 | 0.0 | 0.5 | 0.4150 | nan |
| 0.1962 | 10.0 | 150 | 0.6999 | 0.9962 | 0.9962 | 0.7945 | 0.7945 | 0.4401 | 0.4401 | 0.4783 | 0.0 | 0.5 | 0.4056 | nan |
| 0.1784 | 11.0 | 165 | 0.7335 | 1.0198 | 1.0198 | 0.7969 | 0.7969 | 0.4132 | 0.4132 | 0.5652 | 0.0 | 0.5 | 0.4150 | nan |
| 0.1531 | 12.0 | 180 | 0.8277 | 1.0833 | 1.0833 | 0.8839 | 0.8839 | 0.3378 | 0.3378 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.1425 | 13.0 | 195 | 0.8644 | 1.1070 | 1.1070 | 0.8726 | 0.8726 | 0.3085 | 0.3085 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0921 | 14.0 | 210 | 0.8874 | 1.1217 | 1.1217 | 0.9024 | 0.9024 | 0.2900 | 0.2900 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0913 | 15.0 | 225 | 0.8663 | 1.1083 | 1.1083 | 0.8914 | 0.8914 | 0.3070 | 0.3070 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.08 | 16.0 | 240 | 0.8678 | 1.1093 | 1.1093 | 0.8762 | 0.8762 | 0.3057 | 0.3057 | 0.6522 | 0.0 | 0.5 | 0.5931 | nan |
| 0.0725 | 17.0 | 255 | 0.8497 | 1.0976 | 1.0976 | 0.8868 | 0.8868 | 0.3202 | 0.3202 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0696 | 18.0 | 270 | 0.8533 | 1.1000 | 1.1000 | 0.8796 | 0.8796 | 0.3173 | 0.3173 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0632 | 19.0 | 285 | 0.8563 | 1.1018 | 1.1018 | 0.8768 | 0.8768 | 0.3150 | 0.3150 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0511 | 20.0 | 300 | 0.8433 | 1.0935 | 1.0935 | 0.8684 | 0.8684 | 0.3254 | 0.3254 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0517 | 21.0 | 315 | 0.8449 | 1.0945 | 1.0945 | 0.8758 | 0.8758 | 0.3240 | 0.3240 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0556 | 22.0 | 330 | 0.8305 | 1.0851 | 1.0851 | 0.8469 | 0.8469 | 0.3356 | 0.3356 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0457 | 23.0 | 345 | 0.8369 | 1.0893 | 1.0893 | 0.8555 | 0.8555 | 0.3305 | 0.3305 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0496 | 24.0 | 360 | 0.8441 | 1.0940 | 1.0940 | 0.8648 | 0.8648 | 0.3247 | 0.3247 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0467 | 25.0 | 375 | 0.8470 | 1.0959 | 1.0959 | 0.8633 | 0.8633 | 0.3224 | 0.3224 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0446 | 26.0 | 390 | 0.8562 | 1.1018 | 1.1018 | 0.8708 | 0.8708 | 0.3151 | 0.3151 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0476 | 27.0 | 405 | 0.8600 | 1.1042 | 1.1042 | 0.8714 | 0.8714 | 0.3120 | 0.3120 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.042 | 28.0 | 420 | 0.8657 | 1.1079 | 1.1079 | 0.8763 | 0.8763 | 0.3074 | 0.3074 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan |
| 0.0431 | 29.0 | 435 | 0.8654 | 1.1077 | 1.1077 | 0.8734 | 0.8734 | 0.3077 | 0.3077 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
| 0.0423 | 30.0 | 450 | 0.8646 | 1.1072 | 1.1072 | 0.8721 | 0.8721 | 0.3083 | 0.3083 | 0.5652 | 0.0 | 0.5 | 0.5070 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,449 |
responsibility-framing/predict-perception-bert-cause-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-concept
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. -->
# predict-perception-bert-cause-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4044
- Rmse: 0.6076
- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.6076
- Mae: 0.4548
- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4548
- R2: 0.5463
- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5463
- Cos: 0.2174
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3931
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un concetto astratto (es. gelosia) | Mae | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2 | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.08 | 1.0 | 15 | 0.9520 | 0.9323 | 0.9323 | 0.6560 | 0.6560 | -0.0680 | -0.0680 | 0.0435 | 0.0 | 0.5 | 0.3188 | nan |
| 0.9974 | 2.0 | 30 | 0.8621 | 0.8872 | 0.8872 | 0.5962 | 0.5962 | 0.0328 | 0.0328 | 0.1304 | 0.0 | 0.5 | 0.4066 | nan |
| 0.9337 | 3.0 | 45 | 0.9223 | 0.9176 | 0.9176 | 0.6608 | 0.6608 | -0.0347 | -0.0347 | 0.2174 | 0.0 | 0.5 | 0.3632 | nan |
| 0.966 | 4.0 | 60 | 0.8273 | 0.8691 | 0.8691 | 0.5874 | 0.5874 | 0.0719 | 0.0719 | 0.2174 | 0.0 | 0.5 | 0.3754 | nan |
| 0.8683 | 5.0 | 75 | 0.8741 | 0.8933 | 0.8933 | 0.6136 | 0.6136 | 0.0193 | 0.0193 | 0.2174 | 0.0 | 0.5 | 0.3529 | nan |
| 0.8522 | 6.0 | 90 | 0.7781 | 0.8428 | 0.8428 | 0.5732 | 0.5732 | 0.1271 | 0.1271 | 0.2174 | 0.0 | 0.5 | 0.4152 | nan |
| 0.7968 | 7.0 | 105 | 0.7257 | 0.8139 | 0.8139 | 0.5519 | 0.5519 | 0.1859 | 0.1859 | 0.2174 | 0.0 | 0.5 | 0.4152 | nan |
| 0.7166 | 8.0 | 120 | 0.7122 | 0.8064 | 0.8064 | 0.5792 | 0.5792 | 0.2010 | 0.2010 | 0.1304 | 0.0 | 0.5 | 0.3955 | nan |
| 0.6246 | 9.0 | 135 | 0.6771 | 0.7862 | 0.7862 | 0.5701 | 0.5701 | 0.2403 | 0.2403 | 0.0435 | 0.0 | 0.5 | 0.3955 | nan |
| 0.5205 | 10.0 | 150 | 0.6704 | 0.7823 | 0.7823 | 0.5735 | 0.5735 | 0.2479 | 0.2479 | 0.3913 | 0.0 | 0.5 | 0.4847 | nan |
| 0.4182 | 11.0 | 165 | 0.6852 | 0.7909 | 0.7909 | 0.5987 | 0.5987 | 0.2313 | 0.2313 | 0.3913 | 0.0 | 0.5 | 0.4847 | nan |
| 0.3984 | 12.0 | 180 | 0.6106 | 0.7466 | 0.7466 | 0.5696 | 0.5696 | 0.3150 | 0.3150 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan |
| 0.3138 | 13.0 | 195 | 0.5867 | 0.7318 | 0.7318 | 0.5209 | 0.5209 | 0.3418 | 0.3418 | 0.2174 | 0.0 | 0.5 | 0.3119 | nan |
| 0.2323 | 14.0 | 210 | 0.5120 | 0.6837 | 0.6837 | 0.5007 | 0.5007 | 0.4256 | 0.4256 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.2149 | 15.0 | 225 | 0.4789 | 0.6612 | 0.6612 | 0.4883 | 0.4883 | 0.4627 | 0.4627 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.1753 | 16.0 | 240 | 0.4526 | 0.6428 | 0.6428 | 0.4775 | 0.4775 | 0.4922 | 0.4922 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.1478 | 17.0 | 255 | 0.4383 | 0.6325 | 0.6325 | 0.4616 | 0.4616 | 0.5083 | 0.5083 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.1289 | 18.0 | 270 | 0.4141 | 0.6148 | 0.6148 | 0.4478 | 0.4478 | 0.5355 | 0.5355 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.1035 | 19.0 | 285 | 0.3952 | 0.6007 | 0.6007 | 0.4407 | 0.4407 | 0.5566 | 0.5566 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.1087 | 20.0 | 300 | 0.4217 | 0.6205 | 0.6205 | 0.4505 | 0.4505 | 0.5269 | 0.5269 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.1005 | 21.0 | 315 | 0.4065 | 0.6091 | 0.6091 | 0.4508 | 0.4508 | 0.5440 | 0.5440 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.0868 | 22.0 | 330 | 0.3937 | 0.5995 | 0.5995 | 0.4470 | 0.4470 | 0.5584 | 0.5584 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.0808 | 23.0 | 345 | 0.4132 | 0.6142 | 0.6142 | 0.4617 | 0.4617 | 0.5364 | 0.5364 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.0737 | 24.0 | 360 | 0.4214 | 0.6203 | 0.6203 | 0.4659 | 0.4659 | 0.5272 | 0.5272 | 0.3043 | 0.0 | 0.5 | 0.4066 | nan |
| 0.0711 | 25.0 | 375 | 0.3863 | 0.5939 | 0.5939 | 0.4470 | 0.4470 | 0.5666 | 0.5666 | 0.3043 | 0.0 | 0.5 | 0.3849 | nan |
| 0.066 | 26.0 | 390 | 0.4353 | 0.6304 | 0.6304 | 0.4760 | 0.4760 | 0.5117 | 0.5117 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.0681 | 27.0 | 405 | 0.4078 | 0.6101 | 0.6101 | 0.4612 | 0.4612 | 0.5426 | 0.5426 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.0543 | 28.0 | 420 | 0.4118 | 0.6132 | 0.6132 | 0.4616 | 0.4616 | 0.5380 | 0.5380 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.069 | 29.0 | 435 | 0.4041 | 0.6074 | 0.6074 | 0.4551 | 0.4551 | 0.5466 | 0.5466 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
| 0.0604 | 30.0 | 450 | 0.4044 | 0.6076 | 0.6076 | 0.4548 | 0.4548 | 0.5463 | 0.5463 | 0.2174 | 0.0 | 0.5 | 0.3931 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 11,185 |
responsibility-framing/predict-perception-bert-cause-none | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-cause-none
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. -->
# predict-perception-bert-cause-none
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6269
- Rmse: 1.2763
- Rmse Cause::a Spontanea, priva di un agente scatenante: 1.2763
- Mae: 1.0431
- Mae Cause::a Spontanea, priva di un agente scatenante: 1.0431
- R2: -1.4329
- R2 Cause::a Spontanea, priva di un agente scatenante: -1.4329
- Cos: -0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3371
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Spontanea, priva di un agente scatenante | Mae | Mae Cause::a Spontanea, priva di un agente scatenante | R2 | R2 Cause::a Spontanea, priva di un agente scatenante | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------------:|:------:|:-----------------------------------------------------:|:-------:|:----------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 0.994 | 1.0 | 15 | 0.7156 | 0.8465 | 0.8465 | 0.7809 | 0.7809 | -0.0701 | -0.0701 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 0.9757 | 2.0 | 30 | 0.7096 | 0.8429 | 0.8429 | 0.7666 | 0.7666 | -0.0611 | -0.0611 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 1.0086 | 3.0 | 45 | 0.7779 | 0.8825 | 0.8825 | 0.7981 | 0.7981 | -0.1632 | -0.1632 | -0.0435 | 0.0 | 0.5 | 0.2899 | nan |
| 0.9127 | 4.0 | 60 | 0.8158 | 0.9038 | 0.9038 | 0.8171 | 0.8171 | -0.2199 | -0.2199 | -0.2174 | 0.0 | 0.5 | 0.2975 | nan |
| 0.8555 | 5.0 | 75 | 0.7691 | 0.8775 | 0.8775 | 0.8121 | 0.8121 | -0.1501 | -0.1501 | -0.2174 | 0.0 | 0.5 | 0.3299 | nan |
| 0.8702 | 6.0 | 90 | 0.7818 | 0.8848 | 0.8848 | 0.7781 | 0.7781 | -0.1691 | -0.1691 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 0.76 | 7.0 | 105 | 0.8377 | 0.9158 | 0.9158 | 0.7985 | 0.7985 | -0.2526 | -0.2526 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |
| 0.6997 | 8.0 | 120 | 0.9065 | 0.9527 | 0.9527 | 0.8370 | 0.8370 | -0.3555 | -0.3555 | -0.2174 | 0.0 | 0.5 | 0.3147 | nan |
| 0.5963 | 9.0 | 135 | 1.0611 | 1.0308 | 1.0308 | 0.8396 | 0.8396 | -0.5867 | -0.5867 | -0.0435 | 0.0 | 0.5 | 0.2645 | nan |
| 0.5413 | 10.0 | 150 | 1.1724 | 1.0835 | 1.0835 | 0.8649 | 0.8649 | -0.7532 | -0.7532 | -0.0435 | 0.0 | 0.5 | 0.2645 | nan |
| 0.4994 | 11.0 | 165 | 1.1471 | 1.0717 | 1.0717 | 0.8857 | 0.8857 | -0.7154 | -0.7154 | -0.2174 | 0.0 | 0.5 | 0.3271 | nan |
| 0.4208 | 12.0 | 180 | 1.2136 | 1.1024 | 1.1024 | 0.9392 | 0.9392 | -0.8148 | -0.8148 | -0.2174 | 0.0 | 0.5 | 0.3169 | nan |
| 0.316 | 13.0 | 195 | 1.3499 | 1.1626 | 1.1626 | 0.9395 | 0.9395 | -1.0187 | -1.0187 | -0.2174 | 0.0 | 0.5 | 0.3271 | nan |
| 0.2893 | 14.0 | 210 | 1.4229 | 1.1937 | 1.1937 | 0.9608 | 0.9608 | -1.1278 | -1.1278 | -0.3043 | 0.0 | 0.5 | 0.3269 | nan |
| 0.235 | 15.0 | 225 | 1.4699 | 1.2132 | 1.2132 | 0.9785 | 0.9785 | -1.1981 | -1.1981 | -0.0435 | 0.0 | 0.5 | 0.2865 | nan |
| 0.2397 | 16.0 | 240 | 1.5492 | 1.2455 | 1.2455 | 1.0005 | 1.0005 | -1.3167 | -1.3167 | -0.0435 | 0.0 | 0.5 | 0.2655 | nan |
| 0.1973 | 17.0 | 255 | 1.5541 | 1.2474 | 1.2474 | 1.0165 | 1.0165 | -1.3239 | -1.3239 | -0.0435 | 0.0 | 0.5 | 0.2655 | nan |
| 0.1793 | 18.0 | 270 | 1.4966 | 1.2242 | 1.2242 | 1.0058 | 1.0058 | -1.2380 | -1.2380 | -0.3043 | 0.0 | 0.5 | 0.3437 | nan |
| 0.16 | 19.0 | 285 | 1.4977 | 1.2246 | 1.2246 | 1.0140 | 1.0140 | -1.2396 | -1.2396 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
| 0.1501 | 20.0 | 300 | 1.5751 | 1.2558 | 1.2558 | 1.0254 | 1.0254 | -1.3553 | -1.3553 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
| 0.1342 | 21.0 | 315 | 1.7011 | 1.3051 | 1.3051 | 1.0681 | 1.0681 | -1.5438 | -1.5438 | -0.2174 | 0.0 | 0.5 | 0.2715 | nan |
| 0.137 | 22.0 | 330 | 1.5557 | 1.2481 | 1.2481 | 1.0393 | 1.0393 | -1.3263 | -1.3263 | -0.3043 | 0.0 | 0.5 | 0.3437 | nan |
| 0.11 | 23.0 | 345 | 1.5475 | 1.2448 | 1.2448 | 1.0320 | 1.0320 | -1.3141 | -1.3141 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
| 0.1106 | 24.0 | 360 | 1.6006 | 1.2660 | 1.2660 | 1.0452 | 1.0452 | -1.3936 | -1.3936 | -0.3913 | 0.0 | 0.5 | 0.3297 | nan |
| 0.1013 | 25.0 | 375 | 1.5907 | 1.2621 | 1.2621 | 1.0368 | 1.0368 | -1.3787 | -1.3787 | -0.3043 | 0.0 | 0.5 | 0.2929 | nan |
| 0.0863 | 26.0 | 390 | 1.6436 | 1.2829 | 1.2829 | 1.0496 | 1.0496 | -1.4578 | -1.4578 | -0.3043 | 0.0 | 0.5 | 0.2929 | nan |
| 0.0929 | 27.0 | 405 | 1.6000 | 1.2658 | 1.2658 | 1.0341 | 1.0341 | -1.3927 | -1.3927 | -0.3043 | 0.0 | 0.5 | 0.3245 | nan |
| 0.0829 | 28.0 | 420 | 1.6277 | 1.2767 | 1.2767 | 1.0422 | 1.0422 | -1.4341 | -1.4341 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
| 0.0884 | 29.0 | 435 | 1.6324 | 1.2785 | 1.2785 | 1.0436 | 1.0436 | -1.4411 | -1.4411 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
| 0.0896 | 30.0 | 450 | 1.6269 | 1.2763 | 1.2763 | 1.0431 | 1.0431 | -1.4329 | -1.4329 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,719 |
Jeevesh8/goog_bert_ft_cola-38 | null | Entry not found | 15 |
responsibility-framing/predict-perception-bert-blame-assassin | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-assassin
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. -->
# predict-perception-bert-blame-assassin
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5128
- Rmse: 1.0287
- Rmse Blame::a L'assassino: 1.0287
- Mae: 0.8883
- Mae Blame::a L'assassino: 0.8883
- R2: 0.5883
- R2 Blame::a L'assassino: 0.5883
- Cos: 0.6522
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5795
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a L'assassino | Mae | Mae Blame::a L'assassino | R2 | R2 Blame::a L'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0184 | 1.0 | 15 | 1.2219 | 1.5879 | 1.5879 | 1.4308 | 1.4308 | 0.0191 | 0.0191 | 0.3913 | 0.0 | 0.5 | 0.3781 | nan |
| 0.9214 | 2.0 | 30 | 1.0927 | 1.5017 | 1.5017 | 1.3634 | 1.3634 | 0.1227 | 0.1227 | 0.5652 | 0.0 | 0.5 | 0.4512 | nan |
| 0.7809 | 3.0 | 45 | 0.8206 | 1.3013 | 1.3013 | 1.1808 | 1.1808 | 0.3412 | 0.3412 | 0.4783 | 0.0 | 0.5 | 0.3819 | nan |
| 0.6593 | 4.0 | 60 | 0.5894 | 1.1029 | 1.1029 | 1.0145 | 1.0145 | 0.5268 | 0.5268 | 0.7391 | 0.0 | 0.5 | 0.6408 | nan |
| 0.4672 | 5.0 | 75 | 0.4759 | 0.9910 | 0.9910 | 0.8868 | 0.8868 | 0.6180 | 0.6180 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.3356 | 6.0 | 90 | 0.4220 | 0.9332 | 0.9332 | 0.8083 | 0.8083 | 0.6612 | 0.6612 | 0.6522 | 0.0 | 0.5 | 0.4249 | nan |
| 0.2782 | 7.0 | 105 | 0.4477 | 0.9612 | 0.9612 | 0.8046 | 0.8046 | 0.6406 | 0.6406 | 0.6522 | 0.0 | 0.5 | 0.6101 | nan |
| 0.2075 | 8.0 | 120 | 0.4389 | 0.9518 | 0.9518 | 0.8050 | 0.8050 | 0.6476 | 0.6476 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.1725 | 9.0 | 135 | 0.4832 | 0.9985 | 0.9985 | 0.8356 | 0.8356 | 0.6121 | 0.6121 | 0.7391 | 0.0 | 0.5 | 0.6616 | nan |
| 0.1642 | 10.0 | 150 | 0.4368 | 0.9494 | 0.9494 | 0.8060 | 0.8060 | 0.6493 | 0.6493 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.1172 | 11.0 | 165 | 0.4538 | 0.9677 | 0.9677 | 0.8174 | 0.8174 | 0.6357 | 0.6357 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.104 | 12.0 | 180 | 0.4672 | 0.9819 | 0.9819 | 0.8384 | 0.8384 | 0.6249 | 0.6249 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0822 | 13.0 | 195 | 0.4401 | 0.9530 | 0.9530 | 0.8107 | 0.8107 | 0.6467 | 0.6467 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0755 | 14.0 | 210 | 0.4464 | 0.9598 | 0.9598 | 0.8251 | 0.8251 | 0.6416 | 0.6416 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0801 | 15.0 | 225 | 0.4834 | 0.9988 | 0.9988 | 0.8604 | 0.8604 | 0.6119 | 0.6119 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.053 | 16.0 | 240 | 0.4846 | 1.0001 | 1.0001 | 0.8651 | 0.8651 | 0.6109 | 0.6109 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0573 | 17.0 | 255 | 0.4970 | 1.0128 | 1.0128 | 0.8743 | 0.8743 | 0.6010 | 0.6010 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0571 | 18.0 | 270 | 0.4803 | 0.9956 | 0.9956 | 0.8503 | 0.8503 | 0.6144 | 0.6144 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0483 | 19.0 | 285 | 0.4936 | 1.0093 | 1.0093 | 0.8740 | 0.8740 | 0.6037 | 0.6037 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0414 | 20.0 | 300 | 0.5138 | 1.0297 | 1.0297 | 0.8943 | 0.8943 | 0.5875 | 0.5875 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0513 | 21.0 | 315 | 0.5240 | 1.0399 | 1.0399 | 0.9050 | 0.9050 | 0.5793 | 0.5793 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0499 | 22.0 | 330 | 0.5275 | 1.0434 | 1.0434 | 0.9048 | 0.9048 | 0.5765 | 0.5765 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0423 | 23.0 | 345 | 0.5350 | 1.0508 | 1.0508 | 0.8872 | 0.8872 | 0.5705 | 0.5705 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0447 | 24.0 | 360 | 0.4963 | 1.0120 | 1.0120 | 0.8754 | 0.8754 | 0.6016 | 0.6016 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0364 | 25.0 | 375 | 0.5009 | 1.0167 | 1.0167 | 0.8809 | 0.8809 | 0.5979 | 0.5979 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0412 | 26.0 | 390 | 0.5060 | 1.0219 | 1.0219 | 0.8781 | 0.8781 | 0.5938 | 0.5938 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0297 | 27.0 | 405 | 0.5027 | 1.0185 | 1.0185 | 0.8838 | 0.8838 | 0.5964 | 0.5964 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0416 | 28.0 | 420 | 0.5071 | 1.0230 | 1.0230 | 0.8867 | 0.8867 | 0.5929 | 0.5929 | 0.7391 | 0.0 | 0.5 | 0.4884 | nan |
| 0.0327 | 29.0 | 435 | 0.5124 | 1.0283 | 1.0283 | 0.8883 | 0.8883 | 0.5887 | 0.5887 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
| 0.0383 | 30.0 | 450 | 0.5128 | 1.0287 | 1.0287 | 0.8883 | 0.8883 | 0.5883 | 0.5883 | 0.6522 | 0.0 | 0.5 | 0.5795 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,789 |
responsibility-framing/predict-perception-bert-focus-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-object
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. -->
# predict-perception-bert-focus-object
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2271
- Rmse: 0.5965
- Rmse Focus::a Su un oggetto: 0.5965
- Mae: 0.4372
- Mae Focus::a Su un oggetto: 0.4372
- R2: 0.4957
- R2 Focus::a Su un oggetto: 0.4957
- Cos: 0.6522
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6622
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0371 | 1.0 | 15 | 0.4358 | 0.8263 | 0.8263 | 0.7132 | 0.7132 | 0.0323 | 0.0323 | 0.3043 | 0.0 | 0.5 | 0.3510 | nan |
| 0.9574 | 2.0 | 30 | 0.4420 | 0.8321 | 0.8321 | 0.7175 | 0.7175 | 0.0186 | 0.0186 | 0.3043 | 0.0 | 0.5 | 0.4627 | nan |
| 0.9137 | 3.0 | 45 | 0.4208 | 0.8119 | 0.8119 | 0.6955 | 0.6955 | 0.0657 | 0.0657 | 0.3913 | 0.0 | 0.5 | 0.3928 | nan |
| 0.8465 | 4.0 | 60 | 0.3356 | 0.7251 | 0.7251 | 0.6237 | 0.6237 | 0.2548 | 0.2548 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.6864 | 5.0 | 75 | 0.2876 | 0.6712 | 0.6712 | 0.5624 | 0.5624 | 0.3616 | 0.3616 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.5804 | 6.0 | 90 | 0.3148 | 0.7022 | 0.7022 | 0.5577 | 0.5577 | 0.3011 | 0.3011 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.4983 | 7.0 | 105 | 0.4068 | 0.7983 | 0.7983 | 0.6606 | 0.6606 | 0.0968 | 0.0968 | 0.3913 | 0.0 | 0.5 | 0.4519 | nan |
| 0.3584 | 8.0 | 120 | 0.2567 | 0.6342 | 0.6342 | 0.4883 | 0.4883 | 0.4300 | 0.4300 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan |
| 0.2771 | 9.0 | 135 | 0.2130 | 0.5777 | 0.5777 | 0.4193 | 0.4193 | 0.5270 | 0.5270 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.2135 | 10.0 | 150 | 0.2522 | 0.6285 | 0.6285 | 0.4572 | 0.4572 | 0.4401 | 0.4401 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1654 | 11.0 | 165 | 0.2662 | 0.6457 | 0.6457 | 0.4603 | 0.4603 | 0.4090 | 0.4090 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1554 | 12.0 | 180 | 0.2459 | 0.6207 | 0.6207 | 0.4778 | 0.4778 | 0.4540 | 0.4540 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.1195 | 13.0 | 195 | 0.2385 | 0.6113 | 0.6113 | 0.4618 | 0.4618 | 0.4704 | 0.4704 | 0.5652 | 0.0 | 0.5 | 0.5693 | nan |
| 0.1046 | 14.0 | 210 | 0.2296 | 0.5997 | 0.5997 | 0.4544 | 0.4544 | 0.4903 | 0.4903 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.089 | 15.0 | 225 | 0.2520 | 0.6283 | 0.6283 | 0.4974 | 0.4974 | 0.4404 | 0.4404 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.083 | 16.0 | 240 | 0.2297 | 0.5998 | 0.5998 | 0.4635 | 0.4635 | 0.4901 | 0.4901 | 0.5652 | 0.0 | 0.5 | 0.5610 | nan |
| 0.0701 | 17.0 | 255 | 0.2207 | 0.5879 | 0.5879 | 0.4442 | 0.4442 | 0.5101 | 0.5101 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0585 | 18.0 | 270 | 0.2397 | 0.6128 | 0.6128 | 0.4617 | 0.4617 | 0.4678 | 0.4678 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0652 | 19.0 | 285 | 0.2284 | 0.5981 | 0.5981 | 0.4449 | 0.4449 | 0.4929 | 0.4929 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.059 | 20.0 | 300 | 0.2491 | 0.6247 | 0.6247 | 0.4599 | 0.4599 | 0.4469 | 0.4469 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0464 | 21.0 | 315 | 0.2306 | 0.6010 | 0.6010 | 0.4373 | 0.4373 | 0.4880 | 0.4880 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0529 | 22.0 | 330 | 0.2370 | 0.6093 | 0.6093 | 0.4480 | 0.4480 | 0.4738 | 0.4738 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0555 | 23.0 | 345 | 0.2361 | 0.6082 | 0.6082 | 0.4474 | 0.4474 | 0.4757 | 0.4757 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0447 | 24.0 | 360 | 0.2283 | 0.5980 | 0.5980 | 0.4399 | 0.4399 | 0.4932 | 0.4932 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.046 | 25.0 | 375 | 0.2259 | 0.5948 | 0.5948 | 0.4413 | 0.4413 | 0.4985 | 0.4985 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0379 | 26.0 | 390 | 0.2263 | 0.5953 | 0.5953 | 0.4402 | 0.4402 | 0.4977 | 0.4977 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0438 | 27.0 | 405 | 0.2270 | 0.5963 | 0.5963 | 0.4378 | 0.4378 | 0.4961 | 0.4961 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0354 | 28.0 | 420 | 0.2211 | 0.5886 | 0.5886 | 0.4379 | 0.4379 | 0.5090 | 0.5090 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0363 | 29.0 | 435 | 0.2269 | 0.5962 | 0.5962 | 0.4362 | 0.4362 | 0.4961 | 0.4961 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
| 0.0451 | 30.0 | 450 | 0.2271 | 0.5965 | 0.5965 | 0.4372 | 0.4372 | 0.4957 | 0.4957 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,983 |
responsibility-framing/predict-perception-xlmr-blame-none | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-none
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. -->
# predict-perception-xlmr-blame-none
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8941
- Rmse: 1.1259
- Rmse Blame::a Nessuno: 1.1259
- Mae: 0.8559
- Mae Blame::a Nessuno: 0.8559
- R2: 0.2847
- R2 Blame::a Nessuno: 0.2847
- Cos: 0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3537
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Nessuno | Mae | Mae Blame::a Nessuno | R2 | R2 Blame::a Nessuno | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------:|:------:|:--------------------:|:-------:|:-------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.042 | 1.0 | 15 | 1.2746 | 1.3443 | 1.3443 | 1.1788 | 1.1788 | -0.0197 | -0.0197 | 0.0435 | 0.0 | 0.5 | 0.2970 | nan |
| 0.9994 | 2.0 | 30 | 1.3264 | 1.3714 | 1.3714 | 1.1967 | 1.1967 | -0.0612 | -0.0612 | -0.0435 | 0.0 | 0.5 | 0.2961 | nan |
| 0.9123 | 3.0 | 45 | 1.2511 | 1.3319 | 1.3319 | 1.0932 | 1.0932 | -0.0009 | -0.0009 | 0.1304 | 0.0 | 0.5 | 0.2681 | nan |
| 0.741 | 4.0 | 60 | 1.0204 | 1.2028 | 1.2028 | 0.9818 | 0.9818 | 0.1836 | 0.1836 | 0.3043 | 0.0 | 0.5 | 0.3686 | nan |
| 0.6337 | 5.0 | 75 | 0.8607 | 1.1047 | 1.1047 | 0.8145 | 0.8145 | 0.3115 | 0.3115 | 0.3913 | 0.0 | 0.5 | 0.4044 | nan |
| 0.4974 | 6.0 | 90 | 0.8574 | 1.1026 | 1.1026 | 0.8095 | 0.8095 | 0.3140 | 0.3140 | 0.3913 | 0.0 | 0.5 | 0.4044 | nan |
| 0.4929 | 7.0 | 105 | 0.8548 | 1.1009 | 1.1009 | 0.8560 | 0.8560 | 0.3161 | 0.3161 | 0.3043 | 0.0 | 0.5 | 0.3686 | nan |
| 0.4378 | 8.0 | 120 | 0.6974 | 0.9944 | 0.9944 | 0.7503 | 0.7503 | 0.4421 | 0.4421 | 0.3043 | 0.0 | 0.5 | 0.3686 | nan |
| 0.3999 | 9.0 | 135 | 0.7955 | 1.0620 | 1.0620 | 0.7907 | 0.7907 | 0.3636 | 0.3636 | 0.3913 | 0.0 | 0.5 | 0.4044 | nan |
| 0.3715 | 10.0 | 150 | 0.8954 | 1.1267 | 1.1267 | 0.8036 | 0.8036 | 0.2837 | 0.2837 | 0.4783 | 0.0 | 0.5 | 0.4058 | nan |
| 0.3551 | 11.0 | 165 | 0.8449 | 1.0945 | 1.0945 | 0.8748 | 0.8748 | 0.3241 | 0.3241 | 0.3913 | 0.0 | 0.5 | 0.3931 | nan |
| 0.3428 | 12.0 | 180 | 0.7960 | 1.0624 | 1.0624 | 0.8000 | 0.8000 | 0.3632 | 0.3632 | 0.3913 | 0.0 | 0.5 | 0.4044 | nan |
| 0.2923 | 13.0 | 195 | 0.9027 | 1.1313 | 1.1313 | 0.8441 | 0.8441 | 0.2778 | 0.2778 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.2236 | 14.0 | 210 | 0.8914 | 1.1242 | 1.1242 | 0.8998 | 0.8998 | 0.2869 | 0.2869 | 0.2174 | 0.0 | 0.5 | 0.3324 | nan |
| 0.2553 | 15.0 | 225 | 0.9184 | 1.1411 | 1.1411 | 0.8633 | 0.8633 | 0.2652 | 0.2652 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.2064 | 16.0 | 240 | 0.9284 | 1.1473 | 1.1473 | 0.8919 | 0.8919 | 0.2573 | 0.2573 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1972 | 17.0 | 255 | 0.9495 | 1.1602 | 1.1602 | 0.8768 | 0.8768 | 0.2404 | 0.2404 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1622 | 18.0 | 270 | 0.9850 | 1.1818 | 1.1818 | 0.9303 | 0.9303 | 0.2120 | 0.2120 | 0.2174 | 0.0 | 0.5 | 0.3324 | nan |
| 0.1685 | 19.0 | 285 | 0.9603 | 1.1669 | 1.1669 | 0.8679 | 0.8679 | 0.2317 | 0.2317 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1773 | 20.0 | 300 | 0.9269 | 1.1464 | 1.1464 | 0.8391 | 0.8391 | 0.2585 | 0.2585 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1716 | 21.0 | 315 | 0.8936 | 1.1256 | 1.1256 | 0.8357 | 0.8357 | 0.2851 | 0.2851 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.161 | 22.0 | 330 | 0.8894 | 1.1230 | 1.1230 | 0.8593 | 0.8593 | 0.2884 | 0.2884 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1297 | 23.0 | 345 | 0.8997 | 1.1294 | 1.1294 | 0.8568 | 0.8568 | 0.2802 | 0.2802 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.15 | 24.0 | 360 | 0.8748 | 1.1137 | 1.1137 | 0.8541 | 0.8541 | 0.3002 | 0.3002 | 0.2174 | 0.0 | 0.5 | 0.3324 | nan |
| 0.1149 | 25.0 | 375 | 0.9264 | 1.1461 | 1.1461 | 0.8682 | 0.8682 | 0.2588 | 0.2588 | 0.3913 | 0.0 | 0.5 | 0.3901 | nan |
| 0.1354 | 26.0 | 390 | 0.8829 | 1.1188 | 1.1188 | 0.8608 | 0.8608 | 0.2937 | 0.2937 | 0.2174 | 0.0 | 0.5 | 0.3324 | nan |
| 0.1321 | 27.0 | 405 | 0.9137 | 1.1382 | 1.1382 | 0.8656 | 0.8656 | 0.2691 | 0.2691 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1154 | 28.0 | 420 | 0.8774 | 1.1154 | 1.1154 | 0.8488 | 0.8488 | 0.2980 | 0.2980 | 0.2174 | 0.0 | 0.5 | 0.3324 | nan |
| 0.1112 | 29.0 | 435 | 0.8985 | 1.1287 | 1.1287 | 0.8562 | 0.8562 | 0.2812 | 0.2812 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
| 0.1525 | 30.0 | 450 | 0.8941 | 1.1259 | 1.1259 | 0.8559 | 0.8559 | 0.2847 | 0.2847 | 0.3043 | 0.0 | 0.5 | 0.3537 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,415 |
responsibility-framing/predict-perception-xlmr-cause-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-concept
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. -->
# predict-perception-xlmr-cause-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3933
- Rmse: 0.5992
- Rmse Cause::a Causata da un concetto astratto (es. gelosia): 0.5992
- Mae: 0.4566
- Mae Cause::a Causata da un concetto astratto (es. gelosia): 0.4566
- R2: 0.5588
- R2 Cause::a Causata da un concetto astratto (es. gelosia): 0.5588
- Cos: 0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4340
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un concetto astratto (es. gelosia) | Mae | Mae Cause::a Causata da un concetto astratto (es. gelosia) | R2 | R2 Cause::a Causata da un concetto astratto (es. gelosia) | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------------:|:------:|:----------------------------------------------------------:|:-------:|:---------------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0114 | 1.0 | 15 | 0.9088 | 0.9109 | 0.9109 | 0.6455 | 0.6455 | -0.0195 | -0.0195 | -0.0435 | 0.0 | 0.5 | 0.4027 | nan |
| 1.0 | 2.0 | 30 | 0.8833 | 0.8980 | 0.8980 | 0.6104 | 0.6104 | 0.0090 | 0.0090 | 0.2174 | 0.0 | 0.5 | 0.3681 | nan |
| 0.9533 | 3.0 | 45 | 0.8453 | 0.8785 | 0.8785 | 0.6072 | 0.6072 | 0.0517 | 0.0517 | 0.1304 | 0.0 | 0.5 | 0.3748 | nan |
| 0.9113 | 4.0 | 60 | 0.7797 | 0.8437 | 0.8437 | 0.6024 | 0.6024 | 0.1253 | 0.1253 | 0.0435 | 0.0 | 0.5 | 0.3028 | nan |
| 0.8312 | 5.0 | 75 | 0.5756 | 0.7249 | 0.7249 | 0.5128 | 0.5128 | 0.3542 | 0.3542 | 0.4783 | 0.0 | 0.5 | 0.4572 | nan |
| 0.7224 | 6.0 | 90 | 0.4977 | 0.6741 | 0.6741 | 0.5114 | 0.5114 | 0.4416 | 0.4416 | 0.2174 | 0.0 | 0.5 | 0.4009 | nan |
| 0.5789 | 7.0 | 105 | 0.6338 | 0.7607 | 0.7607 | 0.5059 | 0.5059 | 0.2889 | 0.2889 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.4978 | 8.0 | 120 | 0.3342 | 0.5524 | 0.5524 | 0.4298 | 0.4298 | 0.6250 | 0.6250 | 0.2174 | 0.0 | 0.5 | 0.4274 | nan |
| 0.4572 | 9.0 | 135 | 0.3210 | 0.5413 | 0.5413 | 0.4343 | 0.4343 | 0.6399 | 0.6399 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.3346 | 10.0 | 150 | 0.3456 | 0.5617 | 0.5617 | 0.4198 | 0.4198 | 0.6123 | 0.6123 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.3046 | 11.0 | 165 | 0.3840 | 0.5921 | 0.5921 | 0.4312 | 0.4312 | 0.5692 | 0.5692 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.3035 | 12.0 | 180 | 0.3929 | 0.5989 | 0.5989 | 0.4147 | 0.4147 | 0.5592 | 0.5592 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.2199 | 13.0 | 195 | 0.3165 | 0.5376 | 0.5376 | 0.4065 | 0.4065 | 0.6449 | 0.6449 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.2376 | 14.0 | 210 | 0.3108 | 0.5326 | 0.5326 | 0.3937 | 0.3937 | 0.6514 | 0.6514 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan |
| 0.1639 | 15.0 | 225 | 0.3645 | 0.5769 | 0.5769 | 0.4094 | 0.4094 | 0.5911 | 0.5911 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan |
| 0.1884 | 16.0 | 240 | 0.3762 | 0.5860 | 0.5860 | 0.4398 | 0.4398 | 0.5779 | 0.5779 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1767 | 17.0 | 255 | 0.3805 | 0.5894 | 0.5894 | 0.4540 | 0.4540 | 0.5732 | 0.5732 | 0.2174 | 0.0 | 0.5 | 0.4298 | nan |
| 0.1329 | 18.0 | 270 | 0.3555 | 0.5697 | 0.5697 | 0.4281 | 0.4281 | 0.6011 | 0.6011 | 0.2174 | 0.0 | 0.5 | 0.4298 | nan |
| 0.1834 | 19.0 | 285 | 0.4337 | 0.6292 | 0.6292 | 0.4402 | 0.4402 | 0.5135 | 0.5135 | 0.3913 | 0.0 | 0.5 | 0.4286 | nan |
| 0.1538 | 20.0 | 300 | 0.3554 | 0.5696 | 0.5696 | 0.4236 | 0.4236 | 0.6013 | 0.6013 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1459 | 21.0 | 315 | 0.3592 | 0.5726 | 0.5726 | 0.4348 | 0.4348 | 0.5971 | 0.5971 | 0.3043 | 0.0 | 0.5 | 0.4066 | nan |
| 0.1038 | 22.0 | 330 | 0.3732 | 0.5837 | 0.5837 | 0.4382 | 0.4382 | 0.5813 | 0.5813 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan |
| 0.1432 | 23.0 | 345 | 0.3635 | 0.5760 | 0.5760 | 0.4394 | 0.4394 | 0.5922 | 0.5922 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan |
| 0.1354 | 24.0 | 360 | 0.4359 | 0.6308 | 0.6308 | 0.4793 | 0.4793 | 0.5110 | 0.5110 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1404 | 25.0 | 375 | 0.3919 | 0.5982 | 0.5982 | 0.4650 | 0.4650 | 0.5603 | 0.5603 | 0.3913 | 0.0 | 0.5 | 0.4664 | nan |
| 0.103 | 26.0 | 390 | 0.4223 | 0.6209 | 0.6209 | 0.4691 | 0.4691 | 0.5263 | 0.5263 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1733 | 27.0 | 405 | 0.3972 | 0.6021 | 0.6021 | 0.4591 | 0.4591 | 0.5544 | 0.5544 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1019 | 28.0 | 420 | 0.3958 | 0.6011 | 0.6011 | 0.4593 | 0.4593 | 0.5559 | 0.5559 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.1076 | 29.0 | 435 | 0.4015 | 0.6054 | 0.6054 | 0.4589 | 0.4589 | 0.5496 | 0.5496 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
| 0.0999 | 30.0 | 450 | 0.3933 | 0.5992 | 0.5992 | 0.4566 | 0.4566 | 0.5588 | 0.5588 | 0.3043 | 0.0 | 0.5 | 0.4340 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 11,183 |
responsibility-framing/predict-perception-bert-blame-victim | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-victim
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. -->
# predict-perception-bert-blame-victim
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5075
- Rmse: 0.4599
- Rmse Blame::a La vittima: 0.4599
- Mae: 0.3607
- Mae Blame::a La vittima: 0.3607
- R2: -0.1848
- R2 Blame::a La vittima: -0.1848
- Cos: 0.2174
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.2924
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a La vittima | Mae | Mae Blame::a La vittima | R2 | R2 Blame::a La vittima | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0264 | 1.0 | 15 | 0.4334 | 0.4250 | 0.4250 | 0.3666 | 0.3666 | -0.0119 | -0.0119 | 0.1304 | 0.0 | 0.5 | 0.2703 | nan |
| 0.9814 | 2.0 | 30 | 0.4505 | 0.4333 | 0.4333 | 0.3744 | 0.3744 | -0.0517 | -0.0517 | 0.2174 | 0.0 | 0.5 | 0.2751 | nan |
| 0.9283 | 3.0 | 45 | 0.4349 | 0.4257 | 0.4257 | 0.3627 | 0.3627 | -0.0152 | -0.0152 | 0.1304 | 0.0 | 0.5 | 0.2779 | nan |
| 0.8904 | 4.0 | 60 | 0.4662 | 0.4408 | 0.4408 | 0.3773 | 0.3773 | -0.0884 | -0.0884 | -0.0435 | 0.0 | 0.5 | 0.2681 | nan |
| 0.836 | 5.0 | 75 | 0.4188 | 0.4177 | 0.4177 | 0.3609 | 0.3609 | 0.0223 | 0.0223 | 0.2174 | 0.0 | 0.5 | 0.3051 | nan |
| 0.8293 | 6.0 | 90 | 0.4142 | 0.4155 | 0.4155 | 0.3512 | 0.3512 | 0.0330 | 0.0330 | 0.2174 | 0.0 | 0.5 | 0.3220 | nan |
| 0.7629 | 7.0 | 105 | 0.3837 | 0.3999 | 0.3999 | 0.3387 | 0.3387 | 0.1041 | 0.1041 | 0.2174 | 0.0 | 0.5 | 0.3051 | nan |
| 0.7266 | 8.0 | 120 | 0.3664 | 0.3907 | 0.3907 | 0.3250 | 0.3250 | 0.1446 | 0.1446 | 0.3043 | 0.0 | 0.5 | 0.3409 | nan |
| 0.6121 | 9.0 | 135 | 0.3718 | 0.3936 | 0.3936 | 0.3312 | 0.3312 | 0.1320 | 0.1320 | 0.3043 | 0.0 | 0.5 | 0.3983 | nan |
| 0.5694 | 10.0 | 150 | 0.3679 | 0.3915 | 0.3915 | 0.3197 | 0.3197 | 0.1411 | 0.1411 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan |
| 0.4647 | 11.0 | 165 | 0.3868 | 0.4015 | 0.4015 | 0.3340 | 0.3340 | 0.0970 | 0.0970 | 0.2174 | 0.0 | 0.5 | 0.3285 | nan |
| 0.4212 | 12.0 | 180 | 0.3717 | 0.3936 | 0.3936 | 0.3188 | 0.3188 | 0.1322 | 0.1322 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan |
| 0.3605 | 13.0 | 195 | 0.3437 | 0.3784 | 0.3784 | 0.3066 | 0.3066 | 0.1976 | 0.1976 | 0.3043 | 0.0 | 0.5 | 0.3423 | nan |
| 0.2759 | 14.0 | 210 | 0.3892 | 0.4027 | 0.4027 | 0.3230 | 0.3230 | 0.0914 | 0.0914 | 0.3913 | 0.0 | 0.5 | 0.3518 | nan |
| 0.2868 | 15.0 | 225 | 0.3720 | 0.3937 | 0.3937 | 0.3218 | 0.3218 | 0.1315 | 0.1315 | 0.3913 | 0.0 | 0.5 | 0.3440 | nan |
| 0.2467 | 16.0 | 240 | 0.3881 | 0.4022 | 0.4022 | 0.3291 | 0.3291 | 0.0939 | 0.0939 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan |
| 0.2013 | 17.0 | 255 | 0.4121 | 0.4144 | 0.4144 | 0.3373 | 0.3373 | 0.0380 | 0.0380 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan |
| 0.1966 | 18.0 | 270 | 0.4808 | 0.4476 | 0.4476 | 0.3506 | 0.3506 | -0.1224 | -0.1224 | 0.3913 | 0.0 | 0.5 | 0.3214 | nan |
| 0.177 | 19.0 | 285 | 0.4263 | 0.4215 | 0.4215 | 0.3398 | 0.3398 | 0.0046 | 0.0046 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1589 | 20.0 | 300 | 0.4274 | 0.4220 | 0.4220 | 0.3363 | 0.3363 | 0.0022 | 0.0022 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1488 | 21.0 | 315 | 0.4548 | 0.4353 | 0.4353 | 0.3431 | 0.3431 | -0.0618 | -0.0618 | 0.3043 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1428 | 22.0 | 330 | 0.4405 | 0.4285 | 0.4285 | 0.3417 | 0.3417 | -0.0285 | -0.0285 | 0.3043 | 0.0 | 0.5 | 0.3363 | nan |
| 0.1294 | 23.0 | 345 | 0.4955 | 0.4544 | 0.4544 | 0.3565 | 0.3565 | -0.1568 | -0.1568 | 0.3913 | 0.0 | 0.5 | 0.3440 | nan |
| 0.1291 | 24.0 | 360 | 0.4861 | 0.4501 | 0.4501 | 0.3529 | 0.3529 | -0.1348 | -0.1348 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1187 | 25.0 | 375 | 0.4752 | 0.4450 | 0.4450 | 0.3518 | 0.3518 | -0.1095 | -0.1095 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1141 | 26.0 | 390 | 0.5131 | 0.4624 | 0.4624 | 0.3598 | 0.3598 | -0.1978 | -0.1978 | 0.3043 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1094 | 27.0 | 405 | 0.4863 | 0.4502 | 0.4502 | 0.3547 | 0.3547 | -0.1353 | -0.1353 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.0925 | 28.0 | 420 | 0.4900 | 0.4519 | 0.4519 | 0.3564 | 0.3564 | -0.1439 | -0.1439 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.108 | 29.0 | 435 | 0.5019 | 0.4573 | 0.4573 | 0.3590 | 0.3590 | -0.1719 | -0.1719 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
| 0.1054 | 30.0 | 450 | 0.5075 | 0.4599 | 0.4599 | 0.3607 | 0.3607 | -0.1848 | -0.1848 | 0.2174 | 0.0 | 0.5 | 0.2924 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,752 |
responsibility-framing/predict-perception-bert-blame-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-blame-concept
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. -->
# predict-perception-bert-blame-concept
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7359
- Rmse: 0.6962
- Rmse Blame::a Un concetto astratto o un'emozione: 0.6962
- Mae: 0.5010
- Mae Blame::a Un concetto astratto o un'emozione: 0.5010
- R2: 0.3974
- R2 Blame::a Un concetto astratto o un'emozione: 0.3974
- Cos: 0.3913
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.5507
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un concetto astratto o un'emozione | Mae | Mae Blame::a Un concetto astratto o un'emozione | R2 | R2 Blame::a Un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:-------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0979 | 1.0 | 15 | 1.2387 | 0.9033 | 0.9033 | 0.6603 | 0.6603 | -0.0144 | -0.0144 | 0.0435 | 0.0 | 0.5 | 0.3432 | nan |
| 1.0172 | 2.0 | 30 | 1.1498 | 0.8703 | 0.8703 | 0.5964 | 0.5964 | 0.0584 | 0.0584 | 0.0435 | 0.0 | 0.5 | 0.2935 | nan |
| 0.9879 | 3.0 | 45 | 1.2139 | 0.8942 | 0.8942 | 0.6197 | 0.6197 | 0.0060 | 0.0060 | 0.2174 | 0.0 | 0.5 | 0.4582 | nan |
| 0.9723 | 4.0 | 60 | 1.1152 | 0.8571 | 0.8571 | 0.5982 | 0.5982 | 0.0867 | 0.0867 | 0.2174 | 0.0 | 0.5 | 0.3921 | nan |
| 0.9584 | 5.0 | 75 | 1.0607 | 0.8358 | 0.8358 | 0.5959 | 0.5959 | 0.1314 | 0.1314 | 0.0435 | 0.0 | 0.5 | 0.4165 | nan |
| 0.9023 | 6.0 | 90 | 1.0031 | 0.8128 | 0.8128 | 0.5827 | 0.5827 | 0.1786 | 0.1786 | -0.0435 | 0.0 | 0.5 | 0.3862 | nan |
| 0.8745 | 7.0 | 105 | 0.9715 | 0.7999 | 0.7999 | 0.5796 | 0.5796 | 0.2044 | 0.2044 | 0.3043 | 0.0 | 0.5 | 0.3665 | nan |
| 0.8082 | 8.0 | 120 | 0.8984 | 0.7692 | 0.7692 | 0.5699 | 0.5699 | 0.2643 | 0.2643 | 0.1304 | 0.0 | 0.5 | 0.3390 | nan |
| 0.7475 | 9.0 | 135 | 0.8532 | 0.7497 | 0.7497 | 0.5849 | 0.5849 | 0.3013 | 0.3013 | 0.0435 | 0.0 | 0.5 | 0.3100 | nan |
| 0.6599 | 10.0 | 150 | 0.8737 | 0.7586 | 0.7586 | 0.5822 | 0.5822 | 0.2846 | 0.2846 | 0.3043 | 0.0 | 0.5 | 0.3830 | nan |
| 0.5867 | 11.0 | 165 | 0.8159 | 0.7331 | 0.7331 | 0.5752 | 0.5752 | 0.3318 | 0.3318 | 0.2174 | 0.0 | 0.5 | 0.4439 | nan |
| 0.5081 | 12.0 | 180 | 0.8367 | 0.7424 | 0.7424 | 0.6071 | 0.6071 | 0.3148 | 0.3148 | 0.0435 | 0.0 | 0.5 | 0.3561 | nan |
| 0.4801 | 13.0 | 195 | 0.8353 | 0.7417 | 0.7417 | 0.5567 | 0.5567 | 0.3160 | 0.3160 | 0.3913 | 0.0 | 0.5 | 0.5850 | nan |
| 0.3714 | 14.0 | 210 | 0.8050 | 0.7282 | 0.7282 | 0.5824 | 0.5824 | 0.3408 | 0.3408 | 0.1304 | 0.0 | 0.5 | 0.3975 | nan |
| 0.3306 | 15.0 | 225 | 0.7833 | 0.7183 | 0.7183 | 0.5570 | 0.5570 | 0.3585 | 0.3585 | 0.2174 | 0.0 | 0.5 | 0.4604 | nan |
| 0.2674 | 16.0 | 240 | 0.8148 | 0.7326 | 0.7326 | 0.5475 | 0.5475 | 0.3328 | 0.3328 | 0.3043 | 0.0 | 0.5 | 0.4891 | nan |
| 0.2129 | 17.0 | 255 | 0.8715 | 0.7576 | 0.7576 | 0.5537 | 0.5537 | 0.2863 | 0.2863 | 0.4783 | 0.0 | 0.5 | 0.5017 | nan |
| 0.1924 | 18.0 | 270 | 0.7944 | 0.7234 | 0.7234 | 0.5276 | 0.5276 | 0.3495 | 0.3495 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan |
| 0.1984 | 19.0 | 285 | 0.7885 | 0.7207 | 0.7207 | 0.5208 | 0.5208 | 0.3543 | 0.3543 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.1623 | 20.0 | 300 | 0.7682 | 0.7113 | 0.7113 | 0.5132 | 0.5132 | 0.3709 | 0.3709 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan |
| 0.1409 | 21.0 | 315 | 0.7653 | 0.7100 | 0.7100 | 0.5215 | 0.5215 | 0.3733 | 0.3733 | 0.3043 | 0.0 | 0.5 | 0.5415 | nan |
| 0.1386 | 22.0 | 330 | 0.7688 | 0.7116 | 0.7116 | 0.5124 | 0.5124 | 0.3704 | 0.3704 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.123 | 23.0 | 345 | 0.7756 | 0.7148 | 0.7148 | 0.5144 | 0.5144 | 0.3648 | 0.3648 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.1175 | 24.0 | 360 | 0.7423 | 0.6993 | 0.6993 | 0.5015 | 0.5015 | 0.3921 | 0.3921 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.1188 | 25.0 | 375 | 0.7255 | 0.6913 | 0.6913 | 0.5063 | 0.5063 | 0.4059 | 0.4059 | 0.2174 | 0.0 | 0.5 | 0.4604 | nan |
| 0.1155 | 26.0 | 390 | 0.7635 | 0.7091 | 0.7091 | 0.5083 | 0.5083 | 0.3748 | 0.3748 | 0.4783 | 0.0 | 0.5 | 0.5797 | nan |
| 0.0981 | 27.0 | 405 | 0.7128 | 0.6852 | 0.6852 | 0.5020 | 0.5020 | 0.4163 | 0.4163 | 0.3043 | 0.0 | 0.5 | 0.5415 | nan |
| 0.1109 | 28.0 | 420 | 0.7430 | 0.6996 | 0.6996 | 0.5023 | 0.5023 | 0.3915 | 0.3915 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.1081 | 29.0 | 435 | 0.7367 | 0.6966 | 0.6966 | 0.5007 | 0.5007 | 0.3967 | 0.3967 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
| 0.0953 | 30.0 | 450 | 0.7359 | 0.6962 | 0.6962 | 0.5010 | 0.5010 | 0.3974 | 0.3974 | 0.3913 | 0.0 | 0.5 | 0.5507 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,128 |
responsibility-framing/predict-perception-bert-focus-assassin | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-assassin
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. -->
# predict-perception-bert-focus-assassin
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2964
- Rmse: 0.8992
- Rmse Focus::a Sull'assassino: 0.8992
- Mae: 0.7331
- Mae Focus::a Sull'assassino: 0.7331
- R2: 0.6500
- R2 Focus::a Sull'assassino: 0.6500
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6131
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sull'assassino | Mae | Mae Focus::a Sull'assassino | R2 | R2 Focus::a Sull'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:----------------------------:|:------:|:---------------------------:|:-------:|:--------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0674 | 1.0 | 15 | 0.9851 | 1.6393 | 1.6393 | 1.5316 | 1.5316 | -0.1633 | -0.1633 | 0.1304 | 0.0 | 0.5 | 0.2457 | nan |
| 1.0099 | 2.0 | 30 | 0.8921 | 1.5601 | 1.5601 | 1.4317 | 1.4317 | -0.0535 | -0.0535 | 0.5652 | 0.0 | 0.5 | 0.4734 | nan |
| 0.9295 | 3.0 | 45 | 0.7345 | 1.4155 | 1.4155 | 1.3113 | 1.3113 | 0.1327 | 0.1327 | 0.5652 | 0.0 | 0.5 | 0.3596 | nan |
| 0.8485 | 4.0 | 60 | 0.7282 | 1.4094 | 1.4094 | 1.2678 | 1.2678 | 0.1401 | 0.1401 | 0.7391 | 0.0 | 0.5 | 0.5367 | nan |
| 0.7551 | 5.0 | 75 | 0.5966 | 1.2758 | 1.2758 | 1.1144 | 1.1144 | 0.2955 | 0.2955 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.5563 | 6.0 | 90 | 0.4578 | 1.1175 | 1.1175 | 0.9105 | 0.9105 | 0.4594 | 0.4594 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.4048 | 7.0 | 105 | 0.3539 | 0.9826 | 0.9826 | 0.7770 | 0.7770 | 0.5821 | 0.5821 | 0.6522 | 0.0 | 0.5 | 0.5522 | nan |
| 0.3319 | 8.0 | 120 | 0.2938 | 0.8953 | 0.8953 | 0.7110 | 0.7110 | 0.6530 | 0.6530 | 0.6522 | 0.0 | 0.5 | 0.6021 | nan |
| 0.2224 | 9.0 | 135 | 0.3455 | 0.9708 | 0.9708 | 0.7607 | 0.7607 | 0.5921 | 0.5921 | 0.6522 | 0.0 | 0.5 | 0.3911 | nan |
| 0.1794 | 10.0 | 150 | 0.2719 | 0.8612 | 0.8612 | 0.6768 | 0.6768 | 0.6790 | 0.6790 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1553 | 11.0 | 165 | 0.2855 | 0.8826 | 0.8826 | 0.7053 | 0.7053 | 0.6628 | 0.6628 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.1008 | 12.0 | 180 | 0.3000 | 0.9046 | 0.9046 | 0.7255 | 0.7255 | 0.6458 | 0.6458 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.1121 | 13.0 | 195 | 0.2817 | 0.8766 | 0.8766 | 0.7236 | 0.7236 | 0.6674 | 0.6674 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.08 | 14.0 | 210 | 0.3504 | 0.9777 | 0.9777 | 0.7631 | 0.7631 | 0.5863 | 0.5863 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0802 | 15.0 | 225 | 0.3031 | 0.9094 | 0.9094 | 0.7565 | 0.7565 | 0.6420 | 0.6420 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0685 | 16.0 | 240 | 0.3041 | 0.9109 | 0.9109 | 0.7409 | 0.7409 | 0.6408 | 0.6408 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0592 | 17.0 | 255 | 0.3496 | 0.9767 | 0.9767 | 0.7812 | 0.7812 | 0.5871 | 0.5871 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0625 | 18.0 | 270 | 0.3260 | 0.9430 | 0.9430 | 0.7757 | 0.7757 | 0.6151 | 0.6151 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0589 | 19.0 | 285 | 0.3118 | 0.9222 | 0.9222 | 0.7442 | 0.7442 | 0.6318 | 0.6318 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0518 | 20.0 | 300 | 0.3062 | 0.9140 | 0.9140 | 0.7459 | 0.7459 | 0.6384 | 0.6384 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0456 | 21.0 | 315 | 0.3200 | 0.9344 | 0.9344 | 0.7592 | 0.7592 | 0.6221 | 0.6221 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0477 | 22.0 | 330 | 0.3132 | 0.9244 | 0.9244 | 0.7532 | 0.7532 | 0.6301 | 0.6301 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0448 | 23.0 | 345 | 0.3006 | 0.9056 | 0.9056 | 0.7321 | 0.7321 | 0.6450 | 0.6450 | 0.6522 | 0.0 | 0.5 | 0.5261 | nan |
| 0.0494 | 24.0 | 360 | 0.2985 | 0.9024 | 0.9024 | 0.7463 | 0.7463 | 0.6475 | 0.6475 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0369 | 25.0 | 375 | 0.3039 | 0.9105 | 0.9105 | 0.7359 | 0.7359 | 0.6412 | 0.6412 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0456 | 26.0 | 390 | 0.2989 | 0.9030 | 0.9030 | 0.7210 | 0.7210 | 0.6471 | 0.6471 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.044 | 27.0 | 405 | 0.2997 | 0.9042 | 0.9042 | 0.7418 | 0.7418 | 0.6461 | 0.6461 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0352 | 28.0 | 420 | 0.2970 | 0.9001 | 0.9001 | 0.7346 | 0.7346 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0429 | 29.0 | 435 | 0.2970 | 0.9001 | 0.9001 | 0.7281 | 0.7281 | 0.6493 | 0.6493 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
| 0.0378 | 30.0 | 450 | 0.2964 | 0.8992 | 0.8992 | 0.7331 | 0.7331 | 0.6500 | 0.6500 | 0.7391 | 0.0 | 0.5 | 0.6131 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 8,118 |
responsibility-framing/predict-perception-bert-focus-victim | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-bert-focus-victim
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. -->
# predict-perception-bert-focus-victim
This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2466
- Rmse: 0.6201
- Rmse Focus::a Sulla vittima: 0.6201
- Mae: 0.4936
- Mae Focus::a Sulla vittima: 0.4936
- R2: 0.7293
- R2 Focus::a Sulla vittima: 0.7293
- Cos: 0.8261
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.8155
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sulla vittima | Mae | Mae Focus::a Sulla vittima | R2 | R2 Focus::a Sulla vittima | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0247 | 1.0 | 15 | 1.0286 | 1.2665 | 1.2665 | 1.0280 | 1.0280 | -0.1292 | -0.1292 | 0.1304 | 0.0 | 0.5 | 0.3685 | nan |
| 0.9912 | 2.0 | 30 | 1.0039 | 1.2512 | 1.2512 | 1.0347 | 1.0347 | -0.1020 | -0.1020 | 0.0435 | 0.0 | 0.5 | 0.3333 | nan |
| 0.9147 | 3.0 | 45 | 0.9338 | 1.2067 | 1.2067 | 0.9770 | 0.9770 | -0.0251 | -0.0251 | 0.1304 | 0.0 | 0.5 | 0.3685 | nan |
| 0.8194 | 4.0 | 60 | 0.7641 | 1.0916 | 1.0916 | 0.8476 | 0.8476 | 0.1612 | 0.1612 | 0.4783 | 0.0 | 0.5 | 0.5284 | nan |
| 0.6636 | 5.0 | 75 | 0.6618 | 1.0159 | 1.0159 | 0.8012 | 0.8012 | 0.2735 | 0.2735 | 0.6522 | 0.0 | 0.5 | 0.4741 | nan |
| 0.523 | 6.0 | 90 | 0.5176 | 0.8984 | 0.8984 | 0.7044 | 0.7044 | 0.4318 | 0.4318 | 0.6522 | 0.0 | 0.5 | 0.4741 | nan |
| 0.402 | 7.0 | 105 | 0.3804 | 0.7702 | 0.7702 | 0.6042 | 0.6042 | 0.5824 | 0.5824 | 0.6522 | 0.0 | 0.5 | 0.5395 | nan |
| 0.3401 | 8.0 | 120 | 0.3594 | 0.7487 | 0.7487 | 0.5703 | 0.5703 | 0.6054 | 0.6054 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.2615 | 9.0 | 135 | 0.3429 | 0.7312 | 0.7312 | 0.6049 | 0.6049 | 0.6236 | 0.6236 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.1928 | 10.0 | 150 | 0.2889 | 0.6712 | 0.6712 | 0.5487 | 0.5487 | 0.6828 | 0.6828 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.1703 | 11.0 | 165 | 0.2675 | 0.6458 | 0.6458 | 0.5188 | 0.5188 | 0.7064 | 0.7064 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.1209 | 12.0 | 180 | 0.2826 | 0.6639 | 0.6639 | 0.5475 | 0.5475 | 0.6897 | 0.6897 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.1428 | 13.0 | 195 | 0.2978 | 0.6815 | 0.6815 | 0.5777 | 0.5777 | 0.6731 | 0.6731 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.1038 | 14.0 | 210 | 0.2924 | 0.6753 | 0.6753 | 0.5865 | 0.5865 | 0.6790 | 0.6790 | 0.6522 | 0.0 | 0.5 | 0.2760 | nan |
| 0.0951 | 15.0 | 225 | 0.2905 | 0.6731 | 0.6731 | 0.5750 | 0.5750 | 0.6811 | 0.6811 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.0809 | 16.0 | 240 | 0.2676 | 0.6460 | 0.6460 | 0.5552 | 0.5552 | 0.7062 | 0.7062 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.0811 | 17.0 | 255 | 0.2770 | 0.6572 | 0.6572 | 0.5543 | 0.5543 | 0.6959 | 0.6959 | 0.7391 | 0.0 | 0.5 | 0.6920 | nan |
| 0.0703 | 18.0 | 270 | 0.2634 | 0.6409 | 0.6409 | 0.5251 | 0.5251 | 0.7108 | 0.7108 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0595 | 19.0 | 285 | 0.2638 | 0.6413 | 0.6413 | 0.5196 | 0.5196 | 0.7104 | 0.7104 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0651 | 20.0 | 300 | 0.2520 | 0.6268 | 0.6268 | 0.4970 | 0.4970 | 0.7234 | 0.7234 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0637 | 21.0 | 315 | 0.2668 | 0.6451 | 0.6451 | 0.4965 | 0.4965 | 0.7071 | 0.7071 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0582 | 22.0 | 330 | 0.2455 | 0.6188 | 0.6188 | 0.4759 | 0.4759 | 0.7305 | 0.7305 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0616 | 23.0 | 345 | 0.2509 | 0.6255 | 0.6255 | 0.5084 | 0.5084 | 0.7246 | 0.7246 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0492 | 24.0 | 360 | 0.2510 | 0.6256 | 0.6256 | 0.4985 | 0.4985 | 0.7244 | 0.7244 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0504 | 25.0 | 375 | 0.2512 | 0.6259 | 0.6259 | 0.4849 | 0.4849 | 0.7242 | 0.7242 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0501 | 26.0 | 390 | 0.2585 | 0.6350 | 0.6350 | 0.5140 | 0.5140 | 0.7162 | 0.7162 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0411 | 27.0 | 405 | 0.2544 | 0.6299 | 0.6299 | 0.5148 | 0.5148 | 0.7207 | 0.7207 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.044 | 28.0 | 420 | 0.2466 | 0.6201 | 0.6201 | 0.4964 | 0.4964 | 0.7293 | 0.7293 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.042 | 29.0 | 435 | 0.2466 | 0.6201 | 0.6201 | 0.4836 | 0.4836 | 0.7293 | 0.7293 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.0446 | 30.0 | 450 | 0.2466 | 0.6201 | 0.6201 | 0.4936 | 0.4936 | 0.7293 | 0.7293 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 8,015 |
responsibility-framing/predict-perception-xlmr-blame-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-object
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. -->
# predict-perception-xlmr-blame-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7219
- Rmse: 0.6215
- Rmse Blame::a Un oggetto: 0.6215
- Mae: 0.4130
- Mae Blame::a Un oggetto: 0.4130
- R2: 0.1200
- R2 Blame::a Un oggetto: 0.1200
- Cos: 0.3043
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.4335
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un oggetto | Mae | Mae Blame::a Un oggetto | R2 | R2 Blame::a Un oggetto | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0279 | 1.0 | 15 | 0.8483 | 0.6737 | 0.6737 | 0.4761 | 0.4761 | -0.0341 | -0.0341 | -0.3043 | 0.0 | 0.5 | 0.5507 | nan |
| 1.0676 | 2.0 | 30 | 0.7749 | 0.6439 | 0.6439 | 0.4291 | 0.4291 | 0.0554 | 0.0554 | 0.0435 | 0.0 | 0.5 | 0.2614 | nan |
| 0.9563 | 3.0 | 45 | 0.7765 | 0.6446 | 0.6446 | 0.4349 | 0.4349 | 0.0535 | 0.0535 | -0.0435 | 0.0 | 0.5 | 0.4515 | nan |
| 0.9622 | 4.0 | 60 | 0.7443 | 0.6311 | 0.6311 | 0.4061 | 0.4061 | 0.0927 | 0.0927 | 0.1304 | 0.0 | 0.5 | 0.2933 | nan |
| 0.948 | 5.0 | 75 | 0.8071 | 0.6571 | 0.6571 | 0.3817 | 0.3817 | 0.0162 | 0.0162 | 0.3043 | 0.0 | 0.5 | 0.4207 | nan |
| 0.9532 | 6.0 | 90 | 0.8007 | 0.6546 | 0.6546 | 0.4585 | 0.4585 | 0.0239 | 0.0239 | -0.0435 | 0.0 | 0.5 | 0.5507 | nan |
| 0.9101 | 7.0 | 105 | 0.7126 | 0.6175 | 0.6175 | 0.3649 | 0.3649 | 0.1313 | 0.1313 | 0.4783 | 0.0 | 0.5 | 0.6012 | nan |
| 0.8369 | 8.0 | 120 | 0.7194 | 0.6204 | 0.6204 | 0.3896 | 0.3896 | 0.1231 | 0.1231 | 0.3913 | 0.0 | 0.5 | 0.3494 | nan |
| 0.8062 | 9.0 | 135 | 0.7157 | 0.6188 | 0.6188 | 0.4192 | 0.4192 | 0.1275 | 0.1275 | 0.0435 | 0.0 | 0.5 | 0.3182 | nan |
| 0.7344 | 10.0 | 150 | 0.7161 | 0.6190 | 0.6190 | 0.3612 | 0.3612 | 0.1270 | 0.1270 | 0.3043 | 0.0 | 0.5 | 0.6035 | nan |
| 0.7439 | 11.0 | 165 | 0.5894 | 0.5616 | 0.5616 | 0.3723 | 0.3723 | 0.2816 | 0.2816 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
| 0.6241 | 12.0 | 180 | 0.7087 | 0.6158 | 0.6158 | 0.3972 | 0.3972 | 0.1361 | 0.1361 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
| 0.6123 | 13.0 | 195 | 0.6318 | 0.5814 | 0.5814 | 0.3673 | 0.3673 | 0.2298 | 0.2298 | 0.3913 | 0.0 | 0.5 | 0.4413 | nan |
| 0.5364 | 14.0 | 210 | 0.6504 | 0.5899 | 0.5899 | 0.3674 | 0.3674 | 0.2072 | 0.2072 | 0.3043 | 0.0 | 0.5 | 0.3846 | nan |
| 0.5586 | 15.0 | 225 | 0.7151 | 0.6186 | 0.6186 | 0.3850 | 0.3850 | 0.1283 | 0.1283 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.5133 | 16.0 | 240 | 0.5572 | 0.5460 | 0.5460 | 0.3540 | 0.3540 | 0.3208 | 0.3208 | 0.4783 | 0.0 | 0.5 | 0.5314 | nan |
| 0.4193 | 17.0 | 255 | 0.6047 | 0.5688 | 0.5688 | 0.3710 | 0.3710 | 0.2629 | 0.2629 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3504 | 18.0 | 270 | 0.6103 | 0.5714 | 0.5714 | 0.3687 | 0.3687 | 0.2561 | 0.2561 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3328 | 19.0 | 285 | 0.6181 | 0.5751 | 0.5751 | 0.3915 | 0.3915 | 0.2466 | 0.2466 | 0.4783 | 0.0 | 0.5 | 0.5314 | nan |
| 0.3276 | 20.0 | 300 | 0.6334 | 0.5822 | 0.5822 | 0.3612 | 0.3612 | 0.2279 | 0.2279 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3271 | 21.0 | 315 | 0.6200 | 0.5760 | 0.5760 | 0.3827 | 0.3827 | 0.2442 | 0.2442 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.3139 | 22.0 | 330 | 0.6332 | 0.5821 | 0.5821 | 0.3723 | 0.3723 | 0.2281 | 0.2281 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.2872 | 23.0 | 345 | 0.6694 | 0.5985 | 0.5985 | 0.3966 | 0.3966 | 0.1840 | 0.1840 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3617 | 24.0 | 360 | 0.7022 | 0.6130 | 0.6130 | 0.4061 | 0.4061 | 0.1440 | 0.1440 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3227 | 25.0 | 375 | 0.7364 | 0.6277 | 0.6277 | 0.4205 | 0.4205 | 0.1024 | 0.1024 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.256 | 26.0 | 390 | 0.6938 | 0.6093 | 0.6093 | 0.3833 | 0.3833 | 0.1543 | 0.1543 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.2605 | 27.0 | 405 | 0.7221 | 0.6216 | 0.6216 | 0.4036 | 0.4036 | 0.1198 | 0.1198 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
| 0.2558 | 28.0 | 420 | 0.6959 | 0.6102 | 0.6102 | 0.3859 | 0.3859 | 0.1518 | 0.1518 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.2403 | 29.0 | 435 | 0.7152 | 0.6186 | 0.6186 | 0.4088 | 0.4088 | 0.1281 | 0.1281 | 0.3913 | 0.0 | 0.5 | 0.4924 | nan |
| 0.3263 | 30.0 | 450 | 0.7219 | 0.6215 | 0.6215 | 0.4130 | 0.4130 | 0.1200 | 0.1200 | 0.3043 | 0.0 | 0.5 | 0.4335 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,716 |
responsibility-framing/predict-perception-xlmr-blame-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-concept
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. -->
# predict-perception-xlmr-blame-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9414
- Rmse: 0.7875
- Rmse Blame::a Un concetto astratto o un'emozione: 0.7875
- Mae: 0.6165
- Mae Blame::a Un concetto astratto o un'emozione: 0.6165
- R2: 0.2291
- R2 Blame::a Un concetto astratto o un'emozione: 0.2291
- Cos: 0.1304
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3509
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a Un concetto astratto o un'emozione | Mae | Mae Blame::a Un concetto astratto o un'emozione | R2 | R2 Blame::a Un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------:|:------:|:-----------------------------------------------:|:------:|:----------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0549 | 1.0 | 15 | 1.2093 | 0.8925 | 0.8925 | 0.6659 | 0.6659 | 0.0097 | 0.0097 | -0.3043 | 0.0 | 0.5 | 0.4013 | nan |
| 1.0085 | 2.0 | 30 | 1.2199 | 0.8964 | 0.8964 | 0.6494 | 0.6494 | 0.0010 | 0.0010 | -0.1304 | 0.0 | 0.5 | 0.4515 | nan |
| 1.0131 | 3.0 | 45 | 1.1798 | 0.8815 | 0.8815 | 0.6412 | 0.6412 | 0.0339 | 0.0339 | -0.2174 | 0.0 | 0.5 | 0.2402 | nan |
| 0.9931 | 4.0 | 60 | 1.1726 | 0.8788 | 0.8788 | 0.6370 | 0.6370 | 0.0397 | 0.0397 | -0.1304 | 0.0 | 0.5 | 0.2911 | nan |
| 0.9668 | 5.0 | 75 | 1.1194 | 0.8587 | 0.8587 | 0.5925 | 0.5925 | 0.0833 | 0.0833 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.8759 | 6.0 | 90 | 1.0776 | 0.8425 | 0.8425 | 0.6265 | 0.6265 | 0.1175 | 0.1175 | 0.3043 | 0.0 | 0.5 | 0.4190 | nan |
| 0.8787 | 7.0 | 105 | 1.0513 | 0.8321 | 0.8321 | 0.6087 | 0.6087 | 0.1391 | 0.1391 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.7637 | 8.0 | 120 | 1.0537 | 0.8331 | 0.8331 | 0.6265 | 0.6265 | 0.1372 | 0.1372 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.6568 | 9.0 | 135 | 0.9104 | 0.7744 | 0.7744 | 0.5887 | 0.5887 | 0.2544 | 0.2544 | 0.3043 | 0.0 | 0.5 | 0.3680 | nan |
| 0.6354 | 10.0 | 150 | 0.9055 | 0.7723 | 0.7723 | 0.6222 | 0.6222 | 0.2585 | 0.2585 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan |
| 0.5107 | 11.0 | 165 | 1.0173 | 0.8186 | 0.8186 | 0.6168 | 0.6168 | 0.1669 | 0.1669 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.4598 | 12.0 | 180 | 0.9155 | 0.7765 | 0.7765 | 0.6284 | 0.6284 | 0.2503 | 0.2503 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan |
| 0.3815 | 13.0 | 195 | 0.9255 | 0.7808 | 0.7808 | 0.6140 | 0.6140 | 0.2421 | 0.2421 | 0.1304 | 0.0 | 0.5 | 0.3987 | nan |
| 0.3303 | 14.0 | 210 | 0.8506 | 0.7485 | 0.7485 | 0.6076 | 0.6076 | 0.3035 | 0.3035 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2799 | 15.0 | 225 | 1.0272 | 0.8226 | 0.8226 | 0.6699 | 0.6699 | 0.1588 | 0.1588 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2998 | 16.0 | 240 | 0.9969 | 0.8103 | 0.8103 | 0.6461 | 0.6461 | 0.1836 | 0.1836 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.3131 | 17.0 | 255 | 0.9066 | 0.7727 | 0.7727 | 0.5849 | 0.5849 | 0.2576 | 0.2576 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.2234 | 18.0 | 270 | 0.8741 | 0.7588 | 0.7588 | 0.5953 | 0.5953 | 0.2842 | 0.2842 | 0.2174 | 0.0 | 0.5 | 0.3303 | nan |
| 0.2481 | 19.0 | 285 | 1.0022 | 0.8125 | 0.8125 | 0.6549 | 0.6549 | 0.1793 | 0.1793 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2333 | 20.0 | 300 | 0.9238 | 0.7801 | 0.7801 | 0.6180 | 0.6180 | 0.2435 | 0.2435 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2407 | 21.0 | 315 | 0.9868 | 0.8062 | 0.8062 | 0.6457 | 0.6457 | 0.1919 | 0.1919 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2122 | 22.0 | 330 | 0.9514 | 0.7916 | 0.7916 | 0.6204 | 0.6204 | 0.2209 | 0.2209 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2162 | 23.0 | 345 | 0.9227 | 0.7796 | 0.7796 | 0.6053 | 0.6053 | 0.2444 | 0.2444 | 0.1304 | 0.0 | 0.5 | 0.3509 | nan |
| 0.1739 | 24.0 | 360 | 0.9147 | 0.7762 | 0.7762 | 0.5979 | 0.5979 | 0.2510 | 0.2510 | 0.1304 | 0.0 | 0.5 | 0.3509 | nan |
| 0.2084 | 25.0 | 375 | 0.9645 | 0.7970 | 0.7970 | 0.6296 | 0.6296 | 0.2102 | 0.2102 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.1702 | 26.0 | 390 | 0.9587 | 0.7946 | 0.7946 | 0.6279 | 0.6279 | 0.2149 | 0.2149 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2146 | 27.0 | 405 | 0.9519 | 0.7918 | 0.7918 | 0.6273 | 0.6273 | 0.2205 | 0.2205 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.1645 | 28.0 | 420 | 0.9398 | 0.7868 | 0.7868 | 0.6181 | 0.6181 | 0.2304 | 0.2304 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.2052 | 29.0 | 435 | 0.9492 | 0.7907 | 0.7907 | 0.6228 | 0.6228 | 0.2227 | 0.2227 | 0.0435 | 0.0 | 0.5 | 0.2862 | nan |
| 0.147 | 30.0 | 450 | 0.9414 | 0.7875 | 0.7875 | 0.6165 | 0.6165 | 0.2291 | 0.2291 | 0.1304 | 0.0 | 0.5 | 0.3509 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,062 |
responsibility-framing/predict-perception-xlmr-cause-object | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-object
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. -->
# predict-perception-xlmr-cause-object
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3069
- Rmse: 0.8927
- Rmse Cause::a Causata da un oggetto (es. una pistola): 0.8927
- Mae: 0.5854
- Mae Cause::a Causata da un oggetto (es. una pistola): 0.5854
- R2: 0.5410
- R2 Cause::a Causata da un oggetto (es. una pistola): 0.5410
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6177
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Causata da un oggetto (es. una pistola) | Mae | Mae Cause::a Causata da un oggetto (es. una pistola) | R2 | R2 Cause::a Causata da un oggetto (es. una pistola) | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------------------------------------------------:|:------:|:----------------------------------------------------:|:-------:|:---------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0329 | 1.0 | 15 | 0.8168 | 1.4564 | 1.4564 | 1.2947 | 1.2947 | -0.2216 | -0.2216 | -0.5652 | 0.0 | 0.5 | 0.5993 | nan |
| 1.0096 | 2.0 | 30 | 0.7432 | 1.3893 | 1.3893 | 1.1883 | 1.1883 | -0.1116 | -0.1116 | -0.3913 | 0.0 | 0.5 | 0.6499 | nan |
| 0.9323 | 3.0 | 45 | 0.6879 | 1.3366 | 1.3366 | 1.1054 | 1.1054 | -0.0289 | -0.0289 | -0.1304 | 0.0 | 0.5 | 0.5471 | nan |
| 0.8636 | 4.0 | 60 | 0.6378 | 1.2870 | 1.2870 | 1.0477 | 1.0477 | 0.0461 | 0.0461 | 0.2174 | 0.0 | 0.5 | 0.3007 | nan |
| 0.8041 | 5.0 | 75 | 0.5494 | 1.1945 | 1.1945 | 0.9499 | 0.9499 | 0.1783 | 0.1783 | 0.6522 | 0.0 | 0.5 | 0.6695 | nan |
| 0.7413 | 6.0 | 90 | 0.5526 | 1.1980 | 1.1980 | 0.9503 | 0.9503 | 0.1735 | 0.1735 | 0.5652 | 0.0 | 0.5 | 0.3898 | nan |
| 0.6397 | 7.0 | 105 | 0.4726 | 1.1078 | 1.1078 | 0.7826 | 0.7826 | 0.2932 | 0.2932 | 0.5652 | 0.0 | 0.5 | 0.3257 | nan |
| 0.5556 | 8.0 | 120 | 0.7728 | 1.4167 | 1.4167 | 1.1528 | 1.1528 | -0.1558 | -0.1558 | 0.1304 | 0.0 | 0.5 | 0.4027 | nan |
| 0.4972 | 9.0 | 135 | 0.4375 | 1.0659 | 1.0659 | 0.7577 | 0.7577 | 0.3457 | 0.3457 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan |
| 0.3691 | 10.0 | 150 | 0.4990 | 1.1383 | 1.1383 | 0.8272 | 0.8272 | 0.2537 | 0.2537 | 0.4783 | 0.0 | 0.5 | 0.4781 | nan |
| 0.3381 | 11.0 | 165 | 0.4401 | 1.0690 | 1.0690 | 0.7319 | 0.7319 | 0.3418 | 0.3418 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan |
| 0.2966 | 12.0 | 180 | 0.4794 | 1.1158 | 1.1158 | 0.7835 | 0.7835 | 0.2830 | 0.2830 | 0.5652 | 0.0 | 0.5 | 0.5683 | nan |
| 0.2324 | 13.0 | 195 | 0.4013 | 1.0208 | 1.0208 | 0.6873 | 0.6873 | 0.3998 | 0.3998 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan |
| 0.1848 | 14.0 | 210 | 0.4305 | 1.0574 | 1.0574 | 0.7372 | 0.7372 | 0.3561 | 0.3561 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan |
| 0.1621 | 15.0 | 225 | 0.3652 | 0.9738 | 0.9738 | 0.6164 | 0.6164 | 0.4538 | 0.4538 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.1762 | 16.0 | 240 | 0.3335 | 0.9307 | 0.9307 | 0.6458 | 0.6458 | 0.5012 | 0.5012 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan |
| 0.1404 | 17.0 | 255 | 0.3420 | 0.9424 | 0.9424 | 0.6599 | 0.6599 | 0.4886 | 0.4886 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan |
| 0.1379 | 18.0 | 270 | 0.2853 | 0.8608 | 0.8608 | 0.6063 | 0.6063 | 0.5733 | 0.5733 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan |
| 0.1322 | 19.0 | 285 | 0.3261 | 0.9203 | 0.9203 | 0.6548 | 0.6548 | 0.5123 | 0.5123 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan |
| 0.1067 | 20.0 | 300 | 0.3328 | 0.9296 | 0.9296 | 0.5535 | 0.5535 | 0.5023 | 0.5023 | 0.6522 | 0.0 | 0.5 | 0.6695 | nan |
| 0.1038 | 21.0 | 315 | 0.3066 | 0.8924 | 0.8924 | 0.6266 | 0.6266 | 0.5414 | 0.5414 | 0.4783 | 0.0 | 0.5 | 0.5796 | nan |
| 0.094 | 22.0 | 330 | 0.2924 | 0.8714 | 0.8714 | 0.5792 | 0.5792 | 0.5626 | 0.5626 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.1078 | 23.0 | 345 | 0.3161 | 0.9060 | 0.9060 | 0.6022 | 0.6022 | 0.5272 | 0.5272 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan |
| 0.0976 | 24.0 | 360 | 0.3118 | 0.8998 | 0.8998 | 0.6011 | 0.6011 | 0.5337 | 0.5337 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan |
| 0.0911 | 25.0 | 375 | 0.3123 | 0.9005 | 0.9005 | 0.5811 | 0.5811 | 0.5330 | 0.5330 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.1039 | 26.0 | 390 | 0.3122 | 0.9005 | 0.9005 | 0.5956 | 0.5956 | 0.5330 | 0.5330 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.0775 | 27.0 | 405 | 0.3191 | 0.9103 | 0.9103 | 0.6124 | 0.6124 | 0.5228 | 0.5228 | 0.3913 | 0.0 | 0.5 | 0.5831 | nan |
| 0.0789 | 28.0 | 420 | 0.3135 | 0.9023 | 0.9023 | 0.5825 | 0.5825 | 0.5311 | 0.5311 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.0778 | 29.0 | 435 | 0.3075 | 0.8936 | 0.8936 | 0.5837 | 0.5837 | 0.5401 | 0.5401 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
| 0.082 | 30.0 | 450 | 0.3069 | 0.8927 | 0.8927 | 0.5854 | 0.5854 | 0.5410 | 0.5410 | 0.4783 | 0.0 | 0.5 | 0.6177 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,587 |
responsibility-framing/predict-perception-xlmr-focus-victim | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-victim
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. -->
# predict-perception-xlmr-focus-victim
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2546
- Rmse: 0.6301
- Rmse Focus::a Sulla vittima: 0.6301
- Mae: 0.5441
- Mae Focus::a Sulla vittima: 0.5441
- R2: 0.7205
- R2 Focus::a Sulla vittima: 0.7205
- Cos: 0.8261
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.7802
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sulla vittima | Mae | Mae Focus::a Sulla vittima | R2 | R2 Focus::a Sulla vittima | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0607 | 1.0 | 15 | 0.9261 | 1.2017 | 1.2017 | 0.9557 | 0.9557 | -0.0166 | -0.0166 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
| 1.0107 | 2.0 | 30 | 0.9481 | 1.2159 | 1.2159 | 0.9861 | 0.9861 | -0.0408 | -0.0408 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
| 0.9921 | 3.0 | 45 | 0.9068 | 1.1892 | 1.1892 | 0.9548 | 0.9548 | 0.0045 | 0.0045 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan |
| 0.7769 | 4.0 | 60 | 0.5014 | 0.8842 | 0.8842 | 0.7121 | 0.7121 | 0.4496 | 0.4496 | 0.7391 | 0.0 | 0.5 | 0.6232 | nan |
| 0.5763 | 5.0 | 75 | 0.4019 | 0.7917 | 0.7917 | 0.6737 | 0.6737 | 0.5588 | 0.5588 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan |
| 0.4378 | 6.0 | 90 | 0.3594 | 0.7486 | 0.7486 | 0.5957 | 0.5957 | 0.6055 | 0.6055 | 0.7391 | 0.0 | 0.5 | 0.4442 | nan |
| 0.3595 | 7.0 | 105 | 0.3452 | 0.7337 | 0.7337 | 0.6333 | 0.6333 | 0.6210 | 0.6210 | 0.5652 | 0.0 | 0.5 | 0.2649 | nan |
| 0.3192 | 8.0 | 120 | 0.3275 | 0.7147 | 0.7147 | 0.6205 | 0.6205 | 0.6405 | 0.6405 | 0.7391 | 0.0 | 0.5 | 0.6561 | nan |
| 0.2482 | 9.0 | 135 | 0.2978 | 0.6815 | 0.6815 | 0.5754 | 0.5754 | 0.6731 | 0.6731 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan |
| 0.2416 | 10.0 | 150 | 0.3018 | 0.6860 | 0.6860 | 0.5954 | 0.5954 | 0.6687 | 0.6687 | 0.5652 | 0.0 | 0.5 | 0.2553 | nan |
| 0.2292 | 11.0 | 165 | 0.2764 | 0.6565 | 0.6565 | 0.5522 | 0.5522 | 0.6966 | 0.6966 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
| 0.1752 | 12.0 | 180 | 0.3070 | 0.6920 | 0.6920 | 0.5680 | 0.5680 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan |
| 0.1956 | 13.0 | 195 | 0.2923 | 0.6752 | 0.6752 | 0.5499 | 0.5499 | 0.6791 | 0.6791 | 0.8261 | 0.0 | 0.5 | 0.7843 | nan |
| 0.1424 | 14.0 | 210 | 0.3163 | 0.7023 | 0.7023 | 0.6060 | 0.6060 | 0.6528 | 0.6528 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
| 0.152 | 15.0 | 225 | 0.2436 | 0.6164 | 0.6164 | 0.5127 | 0.5127 | 0.7326 | 0.7326 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
| 0.1277 | 16.0 | 240 | 0.2471 | 0.6208 | 0.6208 | 0.5367 | 0.5367 | 0.7287 | 0.7287 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.1269 | 17.0 | 255 | 0.2573 | 0.6334 | 0.6334 | 0.5329 | 0.5329 | 0.7175 | 0.7175 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.1058 | 18.0 | 270 | 0.2538 | 0.6291 | 0.6291 | 0.5530 | 0.5530 | 0.7214 | 0.7214 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
| 0.107 | 19.0 | 285 | 0.2568 | 0.6328 | 0.6328 | 0.5464 | 0.5464 | 0.7181 | 0.7181 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.1185 | 20.0 | 300 | 0.2452 | 0.6183 | 0.6183 | 0.5317 | 0.5317 | 0.7309 | 0.7309 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
| 0.1029 | 21.0 | 315 | 0.2419 | 0.6142 | 0.6142 | 0.5415 | 0.5415 | 0.7344 | 0.7344 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan |
| 0.0908 | 22.0 | 330 | 0.2462 | 0.6196 | 0.6196 | 0.5261 | 0.5261 | 0.7297 | 0.7297 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.0901 | 23.0 | 345 | 0.2528 | 0.6279 | 0.6279 | 0.5330 | 0.5330 | 0.7225 | 0.7225 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.0979 | 24.0 | 360 | 0.2800 | 0.6607 | 0.6607 | 0.5682 | 0.5682 | 0.6927 | 0.6927 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan |
| 0.0992 | 25.0 | 375 | 0.2502 | 0.6246 | 0.6246 | 0.5517 | 0.5517 | 0.7254 | 0.7254 | 0.6522 | 0.0 | 0.5 | 0.2372 | nan |
| 0.0846 | 26.0 | 390 | 0.2570 | 0.6331 | 0.6331 | 0.5524 | 0.5524 | 0.7178 | 0.7178 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.0717 | 27.0 | 405 | 0.2562 | 0.6321 | 0.6321 | 0.5456 | 0.5456 | 0.7187 | 0.7187 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.0739 | 28.0 | 420 | 0.2570 | 0.6330 | 0.6330 | 0.5471 | 0.5471 | 0.7179 | 0.7179 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.0828 | 29.0 | 435 | 0.2553 | 0.6309 | 0.6309 | 0.5446 | 0.5446 | 0.7198 | 0.7198 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
| 0.086 | 30.0 | 450 | 0.2546 | 0.6301 | 0.6301 | 0.5441 | 0.5441 | 0.7205 | 0.7205 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,981 |
responsibility-framing/predict-perception-xlmr-focus-concept | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-focus-concept
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. -->
# predict-perception-xlmr-focus-concept
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8296
- Rmse: 1.0302
- Rmse Focus::a Su un concetto astratto o un'emozione: 1.0302
- Mae: 0.7515
- Mae Focus::a Su un concetto astratto o un'emozione: 0.7515
- R2: 0.1804
- R2 Focus::a Su un concetto astratto o un'emozione: 0.1804
- Cos: 0.4783
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3415
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un concetto astratto o un'emozione | Mae | Mae Focus::a Su un concetto astratto o un'emozione | R2 | R2 Focus::a Su un concetto astratto o un'emozione | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------------------------------:|:------:|:--------------------------------------------------:|:-------:|:-------------------------------------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0355 | 1.0 | 15 | 0.9822 | 1.1209 | 1.1209 | 0.9649 | 0.9649 | 0.0296 | 0.0296 | 0.2174 | 0.0 | 0.5 | 0.3706 | nan |
| 1.0083 | 2.0 | 30 | 1.1378 | 1.2065 | 1.2065 | 0.9954 | 0.9954 | -0.1241 | -0.1241 | 0.2174 | 0.0 | 0.5 | 0.3309 | nan |
| 0.9823 | 3.0 | 45 | 0.9669 | 1.1121 | 1.1121 | 0.9315 | 0.9315 | 0.0448 | 0.0448 | 0.3043 | 0.0 | 0.5 | 0.3810 | nan |
| 0.9468 | 4.0 | 60 | 0.8856 | 1.0644 | 1.0644 | 0.8584 | 0.8584 | 0.1251 | 0.1251 | 0.3913 | 0.0 | 0.5 | 0.3803 | nan |
| 0.9294 | 5.0 | 75 | 0.8136 | 1.0202 | 1.0202 | 0.8396 | 0.8396 | 0.1963 | 0.1963 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan |
| 0.881 | 6.0 | 90 | 0.7634 | 0.9882 | 0.9882 | 0.8192 | 0.8192 | 0.2458 | 0.2458 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan |
| 0.7589 | 7.0 | 105 | 0.8139 | 1.0204 | 1.0204 | 0.8136 | 0.8136 | 0.1960 | 0.1960 | 0.5652 | 0.0 | 0.5 | 0.4120 | nan |
| 0.7217 | 8.0 | 120 | 0.9105 | 1.0792 | 1.0792 | 0.9394 | 0.9394 | 0.1005 | 0.1005 | 0.3913 | 0.0 | 0.5 | 0.4108 | nan |
| 0.8059 | 9.0 | 135 | 1.0322 | 1.1491 | 1.1491 | 0.9115 | 0.9115 | -0.0197 | -0.0197 | 0.5652 | 0.0 | 0.5 | 0.3738 | nan |
| 0.6483 | 10.0 | 150 | 0.7989 | 1.0109 | 1.0109 | 0.7899 | 0.7899 | 0.2108 | 0.2108 | 0.6522 | 0.0 | 0.5 | 0.4727 | nan |
| 0.5725 | 11.0 | 165 | 0.7175 | 0.9581 | 0.9581 | 0.7011 | 0.7011 | 0.2912 | 0.2912 | 0.5652 | 0.0 | 0.5 | 0.3738 | nan |
| 0.5091 | 12.0 | 180 | 0.8818 | 1.0621 | 1.0621 | 0.8775 | 0.8775 | 0.1289 | 0.1289 | 0.5652 | 0.0 | 0.5 | 0.4063 | nan |
| 0.4526 | 13.0 | 195 | 0.8451 | 1.0398 | 1.0398 | 0.7990 | 0.7990 | 0.1651 | 0.1651 | 0.5652 | 0.0 | 0.5 | 0.4063 | nan |
| 0.361 | 14.0 | 210 | 0.8632 | 1.0508 | 1.0508 | 0.8124 | 0.8124 | 0.1472 | 0.1472 | 0.4783 | 0.0 | 0.5 | 0.3699 | nan |
| 0.3582 | 15.0 | 225 | 0.8461 | 1.0404 | 1.0404 | 0.7923 | 0.7923 | 0.1641 | 0.1641 | 0.3913 | 0.0 | 0.5 | 0.3672 | nan |
| 0.2945 | 16.0 | 240 | 0.9142 | 1.0814 | 1.0814 | 0.8125 | 0.8125 | 0.0968 | 0.0968 | 0.3913 | 0.0 | 0.5 | 0.3672 | nan |
| 0.2891 | 17.0 | 255 | 0.8377 | 1.0352 | 1.0352 | 0.7718 | 0.7718 | 0.1724 | 0.1724 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.2569 | 18.0 | 270 | 0.8106 | 1.0183 | 1.0183 | 0.7481 | 0.7481 | 0.1992 | 0.1992 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.2583 | 19.0 | 285 | 0.8239 | 1.0266 | 1.0266 | 0.7597 | 0.7597 | 0.1861 | 0.1861 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.2217 | 20.0 | 300 | 0.8485 | 1.0419 | 1.0419 | 0.7663 | 0.7663 | 0.1617 | 0.1617 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1927 | 21.0 | 315 | 0.8304 | 1.0307 | 1.0307 | 0.7536 | 0.7536 | 0.1797 | 0.1797 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.176 | 22.0 | 330 | 0.8321 | 1.0317 | 1.0317 | 0.7539 | 0.7539 | 0.1780 | 0.1780 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1639 | 23.0 | 345 | 0.7914 | 1.0062 | 1.0062 | 0.7460 | 0.7460 | 0.2182 | 0.2182 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.177 | 24.0 | 360 | 0.8619 | 1.0500 | 1.0500 | 0.7725 | 0.7725 | 0.1486 | 0.1486 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1473 | 25.0 | 375 | 0.8101 | 1.0180 | 1.0180 | 0.7587 | 0.7587 | 0.1997 | 0.1997 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.181 | 26.0 | 390 | 0.8038 | 1.0141 | 1.0141 | 0.7433 | 0.7433 | 0.2059 | 0.2059 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1679 | 27.0 | 405 | 0.7982 | 1.0105 | 1.0105 | 0.7248 | 0.7248 | 0.2115 | 0.2115 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1529 | 28.0 | 420 | 0.8282 | 1.0293 | 1.0293 | 0.7454 | 0.7454 | 0.1818 | 0.1818 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1822 | 29.0 | 435 | 0.8310 | 1.0311 | 1.0311 | 0.7512 | 0.7512 | 0.1790 | 0.1790 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
| 0.1442 | 30.0 | 450 | 0.8296 | 1.0302 | 1.0302 | 0.7515 | 0.7515 | 0.1804 | 0.1804 | 0.4783 | 0.0 | 0.5 | 0.3415 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 10,359 |
Jeevesh8/goog_bert_ft_cola-39 | null | Entry not found | 15 |
responsibility-framing/predict-perception-xlmr-blame-victim | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-victim
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. -->
# predict-perception-xlmr-blame-victim
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1098
- Rmse: 0.6801
- Rmse Blame::a La vittima: 0.6801
- Mae: 0.5617
- Mae Blame::a La vittima: 0.5617
- R2: -1.5910
- R2 Blame::a La vittima: -1.5910
- Cos: -0.1304
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.3333
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a La vittima | Mae | Mae Blame::a La vittima | R2 | R2 Blame::a La vittima | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------:|:------:|:-----------------------:|:-------:|:----------------------:|:-------:|:----:|:----:|:---------:|:---:|
| 1.0422 | 1.0 | 15 | 0.4952 | 0.4542 | 0.4542 | 0.4095 | 0.4095 | -0.1560 | -0.1560 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 1.0434 | 2.0 | 30 | 0.4851 | 0.4496 | 0.4496 | 0.4054 | 0.4054 | -0.1324 | -0.1324 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 1.038 | 3.0 | 45 | 0.4513 | 0.4337 | 0.4337 | 0.3885 | 0.3885 | -0.0536 | -0.0536 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |
| 1.0151 | 4.0 | 60 | 0.4395 | 0.4280 | 0.4280 | 0.3840 | 0.3840 | -0.0262 | -0.0262 | -0.1304 | 0.0 | 0.5 | 0.2715 | nan |
| 0.9727 | 5.0 | 75 | 0.4490 | 0.4325 | 0.4325 | 0.3811 | 0.3811 | -0.0482 | -0.0482 | 0.2174 | 0.0 | 0.5 | 0.3338 | nan |
| 0.9733 | 6.0 | 90 | 0.4540 | 0.4349 | 0.4349 | 0.3860 | 0.3860 | -0.0598 | -0.0598 | -0.2174 | 0.0 | 0.5 | 0.3248 | nan |
| 0.9396 | 7.0 | 105 | 0.4501 | 0.4331 | 0.4331 | 0.3849 | 0.3849 | -0.0508 | -0.0508 | 0.0435 | 0.0 | 0.5 | 0.2609 | nan |
| 0.8759 | 8.0 | 120 | 0.4597 | 0.4377 | 0.4377 | 0.3849 | 0.3849 | -0.0731 | -0.0731 | 0.3043 | 0.0 | 0.5 | 0.3898 | nan |
| 0.8768 | 9.0 | 135 | 0.4575 | 0.4366 | 0.4366 | 0.3784 | 0.3784 | -0.0680 | -0.0680 | 0.4783 | 0.0 | 0.5 | 0.4615 | nan |
| 0.8312 | 10.0 | 150 | 0.5363 | 0.4727 | 0.4727 | 0.4071 | 0.4071 | -0.2520 | -0.2520 | -0.0435 | 0.0 | 0.5 | 0.2733 | nan |
| 0.7296 | 11.0 | 165 | 0.5291 | 0.4696 | 0.4696 | 0.4057 | 0.4057 | -0.2353 | -0.2353 | 0.3043 | 0.0 | 0.5 | 0.3898 | nan |
| 0.7941 | 12.0 | 180 | 0.5319 | 0.4708 | 0.4708 | 0.4047 | 0.4047 | -0.2417 | -0.2417 | 0.1304 | 0.0 | 0.5 | 0.3381 | nan |
| 0.6486 | 13.0 | 195 | 0.6787 | 0.5318 | 0.5318 | 0.4516 | 0.4516 | -0.5846 | -0.5846 | 0.1304 | 0.0 | 0.5 | 0.3381 | nan |
| 0.6241 | 14.0 | 210 | 1.0146 | 0.6502 | 0.6502 | 0.5580 | 0.5580 | -1.3687 | -1.3687 | -0.1304 | 0.0 | 0.5 | 0.3509 | nan |
| 0.5868 | 15.0 | 225 | 0.7164 | 0.5464 | 0.5464 | 0.4682 | 0.4682 | -0.6725 | -0.6725 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
| 0.5305 | 16.0 | 240 | 0.9064 | 0.6146 | 0.6146 | 0.5173 | 0.5173 | -1.1161 | -1.1161 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
| 0.495 | 17.0 | 255 | 1.3860 | 0.7600 | 0.7600 | 0.6433 | 0.6433 | -2.2358 | -2.2358 | -0.0435 | 0.0 | 0.5 | 0.2935 | nan |
| 0.566 | 18.0 | 270 | 0.7618 | 0.5634 | 0.5634 | 0.4730 | 0.4730 | -0.7785 | -0.7785 | 0.0435 | 0.0 | 0.5 | 0.3225 | nan |
| 0.4305 | 19.0 | 285 | 0.8849 | 0.6072 | 0.6072 | 0.5048 | 0.5048 | -1.0659 | -1.0659 | -0.0435 | 0.0 | 0.5 | 0.3333 | nan |
| 0.5108 | 20.0 | 300 | 0.7376 | 0.5544 | 0.5544 | 0.4716 | 0.4716 | -0.7220 | -0.7220 | 0.0435 | 0.0 | 0.5 | 0.3225 | nan |
| 0.44 | 21.0 | 315 | 1.1611 | 0.6956 | 0.6956 | 0.5921 | 0.5921 | -1.7108 | -1.7108 | -0.1304 | 0.0 | 0.5 | 0.3333 | nan |
| 0.395 | 22.0 | 330 | 1.3004 | 0.7361 | 0.7361 | 0.6078 | 0.6078 | -2.0360 | -2.0360 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.3945 | 23.0 | 345 | 0.9376 | 0.6251 | 0.6251 | 0.5272 | 0.5272 | -1.1890 | -1.1890 | -0.2174 | 0.0 | 0.5 | 0.3188 | nan |
| 0.3093 | 24.0 | 360 | 1.3586 | 0.7524 | 0.7524 | 0.6219 | 0.6219 | -2.1719 | -2.1719 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.2676 | 25.0 | 375 | 1.2200 | 0.7130 | 0.7130 | 0.5994 | 0.5994 | -1.8484 | -1.8484 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.3257 | 26.0 | 390 | 1.2235 | 0.7140 | 0.7140 | 0.5900 | 0.5900 | -1.8564 | -1.8564 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.4004 | 27.0 | 405 | 1.0978 | 0.6763 | 0.6763 | 0.5624 | 0.5624 | -1.5629 | -1.5629 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.283 | 28.0 | 420 | 1.1454 | 0.6909 | 0.6909 | 0.5697 | 0.5697 | -1.6742 | -1.6742 | -0.2174 | 0.0 | 0.5 | 0.3587 | nan |
| 0.3326 | 29.0 | 435 | 1.1214 | 0.6836 | 0.6836 | 0.5646 | 0.5646 | -1.6181 | -1.6181 | -0.1304 | 0.0 | 0.5 | 0.3333 | nan |
| 0.2632 | 30.0 | 450 | 1.1098 | 0.6801 | 0.6801 | 0.5617 | 0.5617 | -1.5910 | -1.5910 | -0.1304 | 0.0 | 0.5 | 0.3333 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,719 |
responsibility-framing/predict-perception-xlmr-blame-assassin | [
"LABEL_0"
] | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-blame-assassin
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. -->
# predict-perception-xlmr-blame-assassin
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4439
- Rmse: 0.9571
- Rmse Blame::a L'assassino: 0.9571
- Mae: 0.7260
- Mae Blame::a L'assassino: 0.7260
- R2: 0.6437
- R2 Blame::a L'assassino: 0.6437
- Cos: 0.7391
- Pair: 0.0
- Rank: 0.5
- Neighbors: 0.6287
- Rsa: nan
## 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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Blame::a L'assassino | Mae | Mae Blame::a L'assassino | R2 | R2 Blame::a L'assassino | Cos | Pair | Rank | Neighbors | Rsa |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------:|:------:|:------------------------:|:------:|:-----------------------:|:------:|:----:|:----:|:---------:|:---:|
| 1.0317 | 1.0 | 15 | 1.1311 | 1.5278 | 1.5278 | 1.3893 | 1.3893 | 0.0919 | 0.0919 | 0.5652 | 0.0 | 0.5 | 0.4512 | nan |
| 0.9475 | 2.0 | 30 | 1.0795 | 1.4926 | 1.4926 | 1.3387 | 1.3387 | 0.1334 | 0.1334 | 0.8261 | 0.0 | 0.5 | 0.6184 | nan |
| 0.9146 | 3.0 | 45 | 1.1092 | 1.5130 | 1.5130 | 1.4078 | 1.4078 | 0.1095 | 0.1095 | 0.4783 | 0.0 | 0.5 | 0.3116 | nan |
| 0.9539 | 4.0 | 60 | 1.1734 | 1.5561 | 1.5561 | 1.4238 | 1.4238 | 0.0580 | 0.0580 | 0.3913 | 0.0 | 0.5 | 0.3614 | nan |
| 0.8665 | 5.0 | 75 | 0.8910 | 1.3560 | 1.3560 | 1.2350 | 1.2350 | 0.2847 | 0.2847 | 0.5652 | 0.0 | 0.5 | 0.4136 | nan |
| 0.6564 | 6.0 | 90 | 0.8469 | 1.3220 | 1.3220 | 1.1570 | 1.1570 | 0.3201 | 0.3201 | 0.3913 | 0.0 | 0.5 | 0.3931 | nan |
| 0.5241 | 7.0 | 105 | 0.6429 | 1.1519 | 1.1519 | 0.9757 | 0.9757 | 0.4838 | 0.4838 | 0.5652 | 0.0 | 0.5 | 0.4222 | nan |
| 0.4589 | 8.0 | 120 | 0.5781 | 1.0923 | 1.0923 | 0.8714 | 0.8714 | 0.5359 | 0.5359 | 0.6522 | 0.0 | 0.5 | 0.4641 | nan |
| 0.4043 | 9.0 | 135 | 0.4525 | 0.9664 | 0.9664 | 0.8257 | 0.8257 | 0.6367 | 0.6367 | 0.5652 | 0.0 | 0.5 | 0.4263 | nan |
| 0.3498 | 10.0 | 150 | 0.4490 | 0.9627 | 0.9627 | 0.8272 | 0.8272 | 0.6395 | 0.6395 | 0.6522 | 0.0 | 0.5 | 0.5144 | nan |
| 0.3505 | 11.0 | 165 | 0.3721 | 0.8763 | 0.8763 | 0.7471 | 0.7471 | 0.7013 | 0.7013 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.3426 | 12.0 | 180 | 0.4117 | 0.9218 | 0.9218 | 0.7477 | 0.7477 | 0.6695 | 0.6695 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.3074 | 13.0 | 195 | 0.3761 | 0.8810 | 0.8810 | 0.7109 | 0.7109 | 0.6981 | 0.6981 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.2261 | 14.0 | 210 | 0.3818 | 0.8877 | 0.8877 | 0.7042 | 0.7042 | 0.6935 | 0.6935 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.2399 | 15.0 | 225 | 0.3893 | 0.8964 | 0.8964 | 0.7108 | 0.7108 | 0.6874 | 0.6874 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.2014 | 16.0 | 240 | 0.4606 | 0.9750 | 0.9750 | 0.8046 | 0.8046 | 0.6302 | 0.6302 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1937 | 17.0 | 255 | 0.4549 | 0.9689 | 0.9689 | 0.7679 | 0.7679 | 0.6348 | 0.6348 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1831 | 18.0 | 270 | 0.4113 | 0.9213 | 0.9213 | 0.6746 | 0.6746 | 0.6698 | 0.6698 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1758 | 19.0 | 285 | 0.4154 | 0.9259 | 0.9259 | 0.7053 | 0.7053 | 0.6665 | 0.6665 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1577 | 20.0 | 300 | 0.3970 | 0.9051 | 0.9051 | 0.7163 | 0.7163 | 0.6813 | 0.6813 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1597 | 21.0 | 315 | 0.4199 | 0.9309 | 0.9309 | 0.7270 | 0.7270 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1145 | 22.0 | 330 | 0.4250 | 0.9365 | 0.9365 | 0.6971 | 0.6971 | 0.6588 | 0.6588 | 0.8261 | 0.0 | 0.5 | 0.6594 | nan |
| 0.1349 | 23.0 | 345 | 0.4168 | 0.9275 | 0.9275 | 0.7126 | 0.7126 | 0.6654 | 0.6654 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1481 | 24.0 | 360 | 0.4421 | 0.9552 | 0.9552 | 0.7441 | 0.7441 | 0.6451 | 0.6451 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1188 | 25.0 | 375 | 0.4356 | 0.9481 | 0.9481 | 0.7444 | 0.7444 | 0.6503 | 0.6503 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1119 | 26.0 | 390 | 0.4456 | 0.9590 | 0.9590 | 0.7139 | 0.7139 | 0.6422 | 0.6422 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1282 | 27.0 | 405 | 0.4456 | 0.9589 | 0.9589 | 0.7637 | 0.7637 | 0.6423 | 0.6423 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.142 | 28.0 | 420 | 0.4501 | 0.9637 | 0.9637 | 0.7146 | 0.7146 | 0.6387 | 0.6387 | 0.8261 | 0.0 | 0.5 | 0.6594 | nan |
| 0.126 | 29.0 | 435 | 0.4442 | 0.9575 | 0.9575 | 0.7189 | 0.7189 | 0.6433 | 0.6433 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
| 0.1308 | 30.0 | 450 | 0.4439 | 0.9571 | 0.9571 | 0.7260 | 0.7260 | 0.6437 | 0.6437 | 0.7391 | 0.0 | 0.5 | 0.6287 | nan |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
| 7,755 |
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