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README.md

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+---
+language: fr
+license: mit
+tags:
+- deberta-v2
+- token-classification
+base_model: almanach/camembertav2-base
+datasets:
+- FSMB
+metrics:
+- las
+- upos
+model-index:
+- name: almanach/camembertav2-base-fsmb
+  results:
+  - task:
+      type: token-classification
+      name: Part-of-Speech Tagging
+    dataset:
+      type: FSMB
+      name: French Social Media Bank
+    metrics:
+    - name: upos
+      type: upos
+      value: 0.95094
+      verified: false
+  - task:
+      type: token-classification
+      name: Dependency Parsing
+    dataset:
+      type: FSMB
+      name: French Social Media Bank
+    metrics:
+    - name: las
+      type: las
+      value: 0.81673
+      verified: false
+---
+
+# Model Card for almanach/camembertav2-base-fsmb
+
+almanach/camembertav2-base-fsmb is a deberta-v2 model for token classification. It is trained on the FSMB dataset for the task of Part-of-Speech Tagging and Dependency Parsing.
+ The model achieves an f1 score of  on the FSMB dataset.
+
+The model is part of the almanach/camembertav2-base family of model finetunes.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Wissam Antoun (Phd Student at Almanach, Inria-Paris)
+- **Model type:** deberta-v2
+- **Language(s) (NLP):** French
+- **License:** MIT
+- **Finetuned from model :** almanach/camembertav2-base
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/WissamAntoun/camemberta
+- **Paper:** https://arxiv.org/abs/2411.08868
+
+## Uses
+
+The model can be used for token classification tasks in French for Part-of-Speech Tagging and Dependency Parsing.
+
+## Bias, Risks, and Limitations
+
+The model may exhibit biases based on the training data. The model may not generalize well to other datasets or tasks. The model may also have limitations in terms of the data it was trained on.
+
+
+## How to Get Started with the Model
+
+You can use the models directly with the hopsparser library in server mode https://github.com/hopsparser/hopsparser/blob/main/docs/server.md
+
+
+## Training Details
+
+### Training Procedure
+
+Model trained with the [hopsparser](https://github.com/hopsparser/hopsparser) library on the FSMB dataset.
+
+
+#### Training Hyperparameters
+
+```yml
+# Layer dimensions
+mlp_input: 1024
+mlp_tag_hidden: 16
+mlp_arc_hidden: 512
+mlp_lab_hidden: 128
+# Lexers
+lexers:
+  - name: word_embeddings
+    type: words
+    embedding_size: 256
+    word_dropout: 0.5
+  - name: char_level_embeddings
+    type: chars_rnn
+    embedding_size: 64
+    lstm_output_size: 128
+  - name: fasttext
+    type: fasttext
+  - name: camembertav2_base_p2_17k_last_layer
+    type: bert
+    model: /scratch/camembertv2/runs/models/camembertav2-base-bf16/post/ckpt-p2-17000/pt/discriminator/
+    layers: [11]
+    subwords_reduction: "mean"
+# Training hyperparameters
+encoder_dropout: 0.5
+mlp_dropout: 0.5
+batch_size: 8
+epochs: 64
+lr:
+  base: 0.00003
+  schedule:
+    shape: linear
+    warmup_steps: 100
+
+```
+
+#### Results
+
+**UPOS:** 0.95094
+**LAS:** 0.81673
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+deberta-v2 custom model for token classification.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@misc{antoun2024camembert20smarterfrench,
+      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection},
+      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
+      year={2024},
+      eprint={2411.08868},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2411.08868},
+}
+
+@inproceedings{grobol:hal-03223424,
+    title = {Analyse en dépendances du français avec des plongements contextualisés},
+    author = {Grobol, Loïc and Crabbé, Benoît},
+    url = {https://hal.archives-ouvertes.fr/hal-03223424},
+    booktitle = {Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles},
+    eventtitle = {TALN-RÉCITAL 2021},
+    venue = {Lille, France},
+    pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
+    hal_id = {hal-03223424},
+    hal_version = {v1},
+}
+
+```

camembertav2_base_p2_17k_last_layer.yaml

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+# Layer dimensions
+mlp_input: 1024
+mlp_tag_hidden: 16
+mlp_arc_hidden: 512
+mlp_lab_hidden: 128
+# Lexers
+lexers:
+  - name: word_embeddings
+    type: words
+    embedding_size: 256
+    word_dropout: 0.5
+  - name: char_level_embeddings
+    type: chars_rnn
+    embedding_size: 64
+    lstm_output_size: 128
+  - name: fasttext
+    type: fasttext
+  - name: camembertav2_base_p2_17k_last_layer
+    type: bert
+    model: /scratch/camembertv2/runs/models/camembertav2-base-bf16/post/ckpt-p2-17000/pt/discriminator/
+    layers: [11]
+    subwords_reduction: "mean"
+# Training hyperparameters
+encoder_dropout: 0.5
+mlp_dropout: 0.5
+batch_size: 8
+epochs: 64
+lr:
+  base: 0.00003
+  schedule:
+    shape: linear
+    warmup_steps: 100

model/config.json

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+{"mlp_input": 1024, "mlp_tag_hidden": 16, "mlp_arc_hidden": 512, "mlp_lab_hidden": 128, "biased_biaffine": true, "default_batch_size": 8, "encoder_dropout": 0.5, "extra_annotations": {}, "labels": ["a_obj", "aff", "arg", "ato", "ats", "aux.caus", "aux.pass", "aux.tps", "bug", "comp", "coord", "de_obj", "dep", "dep.coord", "det", "dis", "meta", "missinghead", "mod", "mod.rel", "obj", "obj.cpl", "obj.p", "obj:mod", "obj:suj:mod", "p_obj", "p_obj.agt", "p_obj.dloc", "p_obj.loc", "p_obj.o", "ponct", "root", "seg", "suj", "suj:ats", "suj:de_obj", "suj:mod", "suj:obj", "suj_a_obj", "suj_ato"], "mlp_dropout": 0.5, "tagset": ["10+euros+", "130+euros+", "800+euros+", "A", "ADJ+NC", "ADJ+PRO", "ADV", "ADV+ADV", "ADV+CLO", "ADV+P", "ADV+V", "C", "CL", "CLO+CLO", "CLO+V", "CLO+VINF", "CLO+VS", "CLOD", "CLR+CLO", "CLR+PREF", "CLR+V", "CLR+VINF", "CLS+CLO", "CLS+CLO+V", "CLS+CLR", "CLS+CLR+CLO", "CLS+V", "CLS+V+ADJ", "CLS+V+PRO", "CS+CLS", "CS+V", "D", "DET+CC", "DET+NC", "DET+NC+TOKEN", "ET", "ET+ET", "HT", "I", "KK", "META", "N", "NC+ADJ", "NC+DET", "NC+P", "P", "P+ADVWH", "P+D", "P+DET", "P+NC", "P+NC+ADJ", "P+PRO", "P+PRO+PROREL", "P+VINF", "PONCT", "PONCT+P", "PREF", "PREF+ADJ", "PRO", "PRO+CLO+V", "PRO+CS", "PRO+PROREL+Y", "PROREL+CLS", "PROREL+V", "PROWH+V+CLS+PROREL", "PROWH+V+CLS+PROREL+CLS", "V", "V+CLO", "V+CLS", "V+CLS+CS", "V+DET+NC", "VIMP+CLO", "VPP+P+NC", "X", "Y", "Y+V", "YM", "_NONE_"], "lexers": {"word_embeddings": "words", "char_level_embeddings": "chars_rnn", "fasttext": "fasttext", "camembertav2_base_p2_17k_last_layer": "bert"}, "multitask_loss": "sum"}

model/lexers/camembertav2_base_p2_17k_last_layer/config.json

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+{"layers": [11], "subwords_reduction": "mean", "weight_layers": false}

model/lexers/camembertav2_base_p2_17k_last_layer/model/config.json

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+{
+  "_name_or_path": "/scratch/camembertv2/runs/models/camembertav2-base-bf16/post/ckpt-p2-17000/pt/discriminator/",
+  "architectures": [
+    "DebertaV2Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 0,
+  "embedding_size": 768,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 1024,
+  "max_relative_positions": -1,
+  "model_name": "camembertav2-base-bf16",
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 0,
+  "vocab_size": 32768
+}

model/lexers/camembertav2_base_p2_17k_last_layer/model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

model/lexers/camembertav2_base_p2_17k_last_layer/model/tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "errors": "replace",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "[UNK]"
+}

model/lexers/char_level_embeddings/config.json

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+{"char_embeddings_dim": 64, "output_dim": 128, "special_tokens": ["<root>"], "charset": ["<pad>", "<special>", "\u0018", "!", "\"", "#", "%", "&", "'", "(", ")", "*", "+", ",", "-", ".", "/", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", ":", ";", "=", ">", "?", "@", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "[", "]", "^", "_", "`", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "~", "\u00ab", "\u00b0", "\u00b7", "\u00bb", "\u00c0", "\u00c7", "\u00c9", "\u00ca", "\u00cf", "\u00e0", "\u00e2", "\u00e7", "\u00e8", "\u00e9", "\u00ea", "\u00ee", "\u00ef", "\u00f4", "\u00f9", "\u00fb", "\u0113", "\u2014", "\u2026", "\u20ac", "\u2122", "\u261d", "\u262e", "\u2665", "\u2705", "\u270b", "\u270c", "\u2764", "\ufe0f", "\ud83d\udc25", "\ud83d\udc4a", "\ud83d\udc4c", "\ud83d\udc4d", "\ud83d\udc4f", "\ud83d\udc51", "\ud83d\udc99", "\ud83d\udc9c", "\ud83d\udcb0", "\ud83d\udcb3", "\ud83d\udd2b", "\ud83d\ude02", "\ud83d\ude03", "\ud83d\ude04", "\ud83d\ude05", "\ud83d\ude08", "\ud83d\ude0a", "\ud83d\ude0d", "\ud83d\ude0e", "\ud83d\ude0f", "\ud83d\ude13", "\ud83d\ude14", "\ud83d\ude16", "\ud83d\ude18", "\ud83d\ude1d", "\ud83d\ude20", "\ud83d\ude21", "\ud83d\ude22", "\ud83d\ude28", "\ud83d\ude2d", "\ud83d\ude31", "\ud83d\ude37", "\ud83d\ude49"]}

model/lexers/fasttext/config.json

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+{"special_tokens": ["<root>"]}

model/lexers/fasttext/fasttext_model.bin

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model/lexers/word_embeddings/config.json

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"Spain-Autocar", "Sph\u00e9tanie", "Sprayberry", "Sp\u00e9ciale", "St", "Star", "Steam", "Steel", "Stiles", "Stockholm", "Street", "Subway", "Suite", "Suits", "Suivez", "Super", "Superbe", "Sur", "Sur-lourd", "Surtout", "Svt", "Swag", "Sympa", "S\u00e9quence", "S\u00e9rieux", "T", "T'", "TA", "TAGGLE", "TE", "TEE-SHIRT", "TEEN", "TETE", "TF1", "THANKS", "THE", "THUG", "TITI", "TL", "TOI", "TOUT", "TP", "TPE", "TPE-PME", "TRES", "TROISIEME", "TROP", "TS", "TT", "TU", "TV", "TVA", "TYL", "Ta", "Talleur", "Taxi", "TeamFCN", "Technology", "Teen", "Teenwolf", "Teint", "Tel", "Tellement", "Tendance", "Terminale", "Terre", "Terrifiant", "Test", "Tfacon", "This", "Thomas", "Thorning", "Titeuf", "Tms45", "Ton", "Tou", "Toujours", "Toumi", "Tout", "Tout_au_long_de", "Toute", "Toutes", "Traitement", "Translation", "Tres", "Trierweiler", "Trkl", "Trois", "Trop", "Trubuil", "Truc", "Tr\u00e8s", "Tr\u00e9s", "Tu", "Tuc", "Tuquet", "Turner", "Tweet", "Twitter", "Tyler", "T\u00caTE", "U", "UN", "USA", "USB", "UV", "Un", "Une", "Uv", "V", "VACANCES", "VAIS", "VEULENT", "VEUX", "VOS", "VOSTFR", "VOTRE", "VOUS", "VS", "Va", "Valls", "Valoche", "Valve", "Van", "Vanessa", "Vazi", "Vegas", "Venez", "Vert", "Victoire", "Victoires", "Vidal", "Viens", "Vient", "Vincent", "Violetta", "Visuellement", "Vita", "Vivement", "Voici", "Voila", "Voil\u00e0", "Voir", "Votre", "Vous", "Vrai", "Vraiment", "Vs", "Vu", "W", "WITHOUT", "WOLF", "WOLVES", "WTF", "Waaaa", "Wall", "Wallah", "Warfare", "Wars", "Waw", "Weetos", "Wii", "WikiLeaks", "Wolf", "Wolfies", "Worldwide", "Wouh", "Wow", "Woyoyoye", "Wssh", "Wtf", "XD", "XIX", "XPTTTDDDR", "XVII", "XVIII", "Y", "YEARS", "Ya", "Yakuza", "Yeah", "Years", "Yeeeeeaaah", "Yeeesssss", "Yohan", "YouTube", "Youtuber", "Yseult", "Yves", "Z", "ZLATAN", "Zineb", "Zita", "Zlatan", "Zoa", "[", "]", "^-^", "^^", "_", "_URL", "__accueil", "`", "a", "aaaah", "aarrrrh", "abandonn\u00e9s", "abattu", "abrazo", "absolument", "abuser", "abusez", "abus\u00e9", "ac", "acces", "accompagne", "accompagnent", "accompagn\u00e9", "accompagn\u00e9e", "accord", "accord\u00e9e", "accouchait", "accoucher", "accroch\u00e9", "accueillir", "acharne", "achete", "acheter", "achet\u00e9", "acn\u00e9", "acronyme", "acteur", "activer", "activit\u00e9", "actrices", "actuel", "actuellement", "actuels", "addicto", "additif", "adepal", "adh\u00e9rer", "adolescence", "adore", "adorerais", "ador\u00e9", "adouci", "adresse", "adresser", "adversaire", "aek", "aerosol", "aerosoles", "afabule", "affabulais", "affaiblies", "affaires", "affiche", "affili\u00e9e", "affreux", "afida", "afin_de", "after", "age", "agence", "agenda", "ago", "agricoles", "agroalimentaire", "agr\u00e9able", "ah", "ahaha", "ahahah", "ahhhhh", "ahs", "ai", "aide", "aider", "aiguiser", "aille", "aimait", "aime", "aimeeeeeeeee", "aiment", "aimer", "aimerais", "aimeras", "aimerons", "aimes", "aim\u00e9", "ainsi_que", "air", "ais", "aisselles", "ajouter", "ak", "alarmants", "album", "alcool", "alcoolisme", "alcools", "algerie", "algerien", "alg\u00e9rien", "aliz\u00e9e", "allah", "allais", "allait", "allemandes", "allemands", "aller", "allers", "allers-retours", "allez", "alliance", "allions", "allo", "allonger", "all\u00e9s", "alors", "alors_qu'", "alors_que", "alrs", "alterner", "amande", "ambiance", "ambulatoire", "amen", "ami", "amicalemen", "amie", "amis", "amiti\u00e9", "amiti\u00e9s", "amour", "amoureuse", "amoureux", "amplement", "ampleur", "amusement", "amuser", "am\u00e8liorer", "am\u00e9liorant", "am\u00e9liorer", "am\u00e9ricaine", "am\u00e9ricains", "an", "analyses", "anatomy", "ancienne", "and", "anelka", "ange", "anglais", "angoise", "angoisse", "animal", "animaleries", "animaux", "anineLTounissi", "annalis\u00e9", "anneau", "annee", "annexes", "annex\u00e9e", "annif", "anniversaire", "annonce", "annoncions", "annonc\u00e9", "ann\u00e9e", "ann\u00e9es", "ann\u00e9s", "[email protected]", "anorexiques", "ans", "anti-", "antibio", "antibiotique", "antisystemite", "an\u00e9anti", "apercois", "apercoit", "aper\u00e7oit", "aper\u00e7u", "apitoyer", "app", "apparemment", "apparition", "appart", "appartement", "appeler", "appelle", "appellerait", "appelles", "appli", "applicable", "appliquant", "appliquer", "apporte", "apprend", "apprendre", "appris", "apprivoisable", "approcher", "appropri\u00e9", "appr\u00e9ci\u00e9", "appuyer", "aprem", "apres", "aprs", "apr\u00e8m", "apr\u00e8s", "apr\u00e8s_que", "apr\u00e9s", "ap\u00e9ro", "arabe", "arbitres", "arbres", "archi", "architectural", "are", "ares", "arguments", "aromatiques", "arranger", "arret", "arrete", "arreter", "arretez", "arret\u00e9", "arris\u00e9s", "arrivait", "arrive", "arrivent", "arriver", "arrivera", "arriverais", "arriverons", "arrivez", "arriviez", "arriv\u00e9", "arriv\u00e9e", "arri\u00e8re", "arr\u00eat", "arr\u00eate", "arr\u00eater", "arr\u00eates", "arr\u00eatez", "arr\u00eat\u00e9", "art", "artifices", "artisans", "artiste", "as", "asiatiques", "assassin\u00e9", "assembl\u00e9e", "assez", "assise", "assistante", "assome", "assum\u00e9es", "assure", "assur\u00e9e", "ass\u00e9", "astuce", "astuces", "at", "atelier", "atendez", "attaque", "attaquer", "atteint", "atteinte", "atten", "attend", "attendais", "attendant", "attendre", "attendue", "attente", "attention", "attirance", "attire", "attitude", "attractive", "att\u00e9nuer", "au", "au-dessus_du", "au_bout_d'", "au_bout_de", "au_bout_des", "au_contraire", "au_lieu_d'", "au_milieu_de", "au_moins", "au_niveau_de", "au_niveau_des", "au_niveau_du", "au_vu_des", "aucun", "aucune", "audacieuse", "audiofanzine", "auetofficiel", "augmenter", "augure", "auhourd", "auj", "aujourd'_hui", "aura", "aurai", "aurais", "aurait", "auras", "auriez", "aurl86", "aussi", "autant", "auto-", "autoriser", "autour_de", "autre", "autrement", "autres", "aux", "avaient", "avais", "avait", "avance", "avant", "avant-premi\u00e8re", "avant_d'", "avant_de", "avant_que", "avantage", "avc", "ave", "avec", "avenir", "aventures", "aver", "avez", "avion", "avis", "avk", "avoare", "avoir", "avoirs", "avon", "avons", "avoue", "avouer", "avouons", "av\u00e9", "ayant", "ayez", "a\u00e9rosoles", "b", "ba", "babtou", "back", "bacteries", "bad", "bah", "bahahaha", "bahut", "baigne", "baigner", "bail", "bails", "bain", "baiser", "baisser", "ballon", "ban", "bancaire", "bande", "bande-annonce", "banlieues", "bar", "barbecue", "barbouiller", "bariol\u00e9", "barre", "barres", "bars", "bas", "base", "basiques", "basse", "bassin", "bateau", "baterie", "bats", "battles", "battre", "battu", "baucou", "bavarde", "bb", "bcp", "be", "beau", "beaucoup", "beautiful", "beaut\u00e9", "beetroots", "beh", "bel", "belaaaaaaak", "bele", "belieber", "belle", "belles", "bellllllllllllllllllle", "besoin", "best", "beta", "beugue", "beurette", "bff", "bg", "bien", "bien_s\u00fbr", "bientot", "bient\u00f4t", "bienvenue", "bienvnu", "biieeeen", "bijou", "bilan", "bilay", "bim", "bin", "biometrique", "biopsie", "biotique", "biquettes", "bird", "bise", "bisou", "bisous", "bisoussssssssssssssssssssssssssssssssssssssssss", "bit.ly/A3Qc01", "bit.ly/xGGnD6", "bit.ly/xgkM9m", "bit.ly/z5NJ6e", "bit.ly/zNrvmD", "bitch", "bizarre", "bizarrement", "bi\u00e8res", "bla", "black", "blackberry", "blacks", "blague", "blanc", "blanche", "blanchissement", "blancs", "bled", "blender", "bleu", "bleues", "bleus", "blinde", "blog", "blon", "bloody", "bl\u00e9", "boeuf", "bof", "boir", "boirai", "boire", "bois", "boit", "boite", "bokits", "bol", "boloss", "bon", "bond", "bone", "bonhomme", "bonjoir", "bonjour", "bonjours", "bonne", "bonnes", "bons", "bonsoir", "bord", "bordel", "bords", "boss", "bosse", "bosser", "botox\u00e9", "boucle", "bouffer", "bouffonne", "bouge", "bouger", "bougzer", "bouillonnante", "boulet", "bouquins", "bout", "bouteilles", "boutin", "bouton", "boutons", "bo\u00eete", "bo\u00eetes", "branche", "branchement", "branch\u00e9", "bras", "bravOOOOOOOOOOOOOOOOOooooooooooooooooooooooooo", "bravo", "bravoooo", "break", "bredouille", "bref", "breff", "breizh", "breizhoneg", "bretons", "bris\u00e9s", "bronchite", "bronz\u00e9", "brosse", "broussaille", "bruit", "brune", "brutal", "bu", "budget", "bug", "buisson", "bulles", "burgers", "burqua", "bus", "but", "bute", "buts", "bz", "b\u00e2bord", "b\u00e2tard", "b\u00e9n\u00e9ficier", "b\u00e9n\u00e9fiques", "b\u00eale", "b\u00eate", "b\u00eates", "c", "c'", "ca", "cable", "caca", "cacededi", "cacheton", "cach\u00e9", "cadeau", "cadeaux", "caen", "caf\u00e9", "cales", "call", "calme", "calotte", "camionnette", "campagne", "canada", "canards", "canaries", "canaris", "cancer", "candy", "cap", "capable", "capitaine", "capitaliser", "capter", "capture", "capuche", "car", "caract\u00e8re", "caract\u00e8res", "carri\u00e8re", "carr\u00e9ment", "carte", "cartes", "cartonner", "cas", "cash", "casino", "casse", "casser", "casserole", "cass\u00e9", "cast", "casting", "catalogu\u00e9", "catastrophe", "cat\u00e9gorie", "cauet", "cause", "cc", "cd", "ce", "ceci", "ceinture", "cela", "celebr\u00e9", "celib", "celle", "celui", "cel\u00e0", "censur\u00e9", "cens\u00e9", "cent", "centre", "cercle", "certain", "certains", "certes", "cerveau", "cerveaux", "cervicale", "cervicales", "ces", "cesse", "cest", "cet", "cette", "ceux", "chaises", "chaleur", "chaleureux", "challenge", "chambre", "chambres", "championnat", "champions", "chance", "chances", "change", "changement", "changer", "changez", "chanson", "chansons", "chantait", "chante", "chanter", "chanteur", "chant\u00e9", "chapitre", "chaque", "chaque_fois_que", "charge", "charisme", "charmante", "chasseur", "chat", "chats", "chatte", "chatter", "chaud", "chaussettes", "chaussures", "cha\u00eene", "cheat", "check", "chef", "chemin", "chenter", "cher", "cherche", "cherchent", "chercher", "cherches", "cheval", "chevaucher", "cheveu", "cheveux", "chez", "chicha", "chichis", "chien", "chiens", "chier", "chimie", "chimio", "chiottes", "choc", "chocolat", "chocote", "choisi", "choisis", "choix", "chose", "choses", "chou", "choux", "chronique", "chu", "chui", "ch\u00e8vre", "ch\u00e8vres", "ch\u00e9rie", "ch\u00f4mages", "ci", "cicatrices", "cinq", "cinqui\u00e8me", "cin\u00e9", "citent", "cites", "cit\u00e9", "cit\u00e9s", "civique", "cladribine", "claire", "clairement", "claque", "claquer", "classe", "classeur", "clearblu*", "clef", "client", "clins", "clips", "clou", "cloud", "club", "cl\u00e9", "cm", "cmme", "coca\u00efne", "cocktails", "coco", "cocue", "cod6", "code", "coeur", "coiffer", "coiffeur", "coiffure", "coke", "colis", "collait", "colle", "collectionnent", "collegue", "collegues", "coller", "colll", "coll\u00e9", "coll\u00e9giens", "coll\u00e9s", "colombe", "coloniale", "colonisation", "coloration", "col\u00e8re", "com", "comat\u00e9", "combat", "combien", "come", "comm", "command\u00e9", "comme", "commence", "commencent", "commencer", "commenc\u00e9", "comment", "commentaire", "commentaires", "commente", "commerce", "commercialis\u00e9", "commun", "communiquant", "comne", "compagne", "compagnie", "compagnon", "comparer", "compar\u00e9", "compar\u00e9e", "compatible", "compatriote", "compliquee", "compliqu\u00e9", "compl\u00e8te", "compl\u00e8tement", "comportement", "comprend", "comprendre", "comprends", "comprenez", "comprennent", "compren\u00e9", "compri", "compris", "comprise", "compr\u00e9hensible", "compte", "compter", "comptes", "comptez", "comp\u00e9tents", "comp\u00e9tition", "con", "conai", "concentration", "concentr\u00e9", "concernant", "concerne", "concert", "conduire", "conduit", "confiance", "confiante", "confirmation", "confirmer", "confiture", "confonde", "confortable", "cong\u00e9s-indemnit\u00e9s", "connai", "connais", "connaissais", "connaissent", "connaissez", "connait", "connaitre", "connard", "connecter", "connu", "connue", "cons", "consacr\u00e9", "conseil", "conseille", "conseill\u00e9", "conseils", "conserver", "considere", "consid\u00e9ration", "consid\u00e9rez", "consistance", "consolation", "consoler", "consommation", "consommer", "constamment", "constructives", "consultation", "consulter", "consulterai", "consult\u00e9", "contact", "contacter", "contact\u00e9", "contant", "conte", "conten", "content", "contentais", "contente", "contents", "contiennent", "continue", "continuent", "continuer", "continu\u00e9", "contourner", "contraception", "contract\u00e9", "contraignant", "contraignante", "contre", "contre-attaque", "contredire", "contrefous", "contribu\u00e9", "controle", "controler", "control\u00e9", "contr\u00f4le", "contr\u00f4les", "convaincu", "convenir", "convergence", "conversation", "convertibles", "conviens", "convivial", "convoqu\u00e9", "cool", "cooool", "coordonn\u00e9es", "cop", "copain", "copains", "copine", "copines", "coq", "cordon", "cornes", "corps", "correspondre", "corse", "cosse", "cote", "coter", "cotise", "cot\u00e9", "cot\u00e9s", "cou", "couche", "coucher", "couch\u00e9", "coucou", "couilles", "coule", "couleur", "couleurs", "couloirs", "coup", "coupe", "couple", "coups", "cour", "courage", "courageuse", "couraient", "courant", "courbatur\u00e9", "courbe", "courir", "courronner", "cours", "course", "courses", "courtois", "couru", "couscous", "cousin", "cousine", "coute", "coutumes", "couve", "couzin", "cover", "co\u00e9quipier", "co\u00fbte", "co\u00fbtent", "cpe", "cqqn", "cradav", "crade", "crainte", "crampe", "crampes", "craqu\u00e9", "crasseuse", "creme", "crie", "crise", "criteres", "critique", "critiquent", "critiquer", "critiques", "cri\u00e9", "cro", "croient", "croir", "croire", "crois", "croisera", "croit", "croustillante", "croyais", "croyance", "cru", "crues", "crumble", "crush", "cr\u00e2ne", "cr\u00e8che", "cr\u00e8ve", "cr\u00e9ateur", "cr\u00e9ation", "cr\u00e9dit", "cr\u00e9er", "cr\u00e9mailli\u00e8re", "cr\u00e9tin", "cr\u00e9tins", "cr\u00e9\u00e9", "cs", "cst", "ct", "cuill\u00e8re", "cuir", "cuire", "cuisine", "cuisse", "cuit", "cul", "culinaires", "culpabilis\u00e9", "cure", "curieux", "curiosit\u00e9", "cycle", "cygne", "cylindres", "c\u00e2bles", "c\u00e2lins", "c\u00e8", "c\u00e9d\u00e9", "c\u00e9l\u00e9bration", "c\u00e9r\u00e9ales", "c\u00e9sar", "c\u00e9t\u00e9", "c\u00e9wie", "c\u00f4te", "c\u00f4t\u00e9", "d", "d'", "d'_abord", "d'_accord", "d'_ailleurs", "d'_autant_que", "d'_autres", "d-d-d-d-uel", "d1", "dab", "daleeeu", "daller", "dan", "dance", "daniel", "dans", "danser", "dantesque", "dar", "daron", "date", "davantage", "davoir", "day", "dc", "dcke", "de", "de_la_part_de", "de_plus_en_plus", "dear", "deballe", "debrouiller", "debu", "debut", "decembre", "decider", "declique", "decolle", "deconner", "decu", "degonfl\u00e9e", "degout\u00e9", "degr\u00e9", "deh", "dehors", "deja", "delle", "demain", "demandais", "demande", "demandent", "demander", "demand\u00e9", "demaquiller", "demi", "dent", "dents", "depasse", "depasser", "deplier", "depresseur", "deprimer", "depuis", "depuis_que", "derby", "dernier", "derniere", "dernierement", "derni\u00e8re", "derni\u00e8res", "derriere", "derri\u00e8re", "des", "descende", "desesp\u00e8re", "desimlocker", "desinstalle", "desirent", "desocialiser", "desserts", "dessous", "dessus", "destination", "deten", "determineront", "deteste", "deux", "deuxi\u00e8me", "deuzio", "devant", "devenir", "devenu", "devenue", "devez", "devien", "deviennent", "deviens", "devient", "devnir", "devoir", "devoirs", "devraient", "devrais", "devrait", "df", "dfair", "dfaire", "di", "diabolique", "diagnoistiqu\u00e9e", "diagnostiqu\u00e9", "dialogues", "dictionnaire", "didentiter", "dieu", "diffenbachia", "different", "difficile", "difficiles", "difficle", "difficult\u00e9s", "diff\u00e9rence", "diff\u00e9rent", "diff\u00e9rentes", "diff\u00e9rents", "diiiiiingue", "diit", "dilemme", "dimanche", "ding", "dingue", "diplome", "dirais", "dirait", "dire", "direct", "directement", "directeur", "dirigez", "dis", "disait", "disant", "disatnce", "discours", "discourt", "discr\u00e9tion", "discuter", "dise", "disent", "disiez", "disparu", "disponible", "dispose", "disques", "dissertation", "dissip\u00e9s", "distance", "distence", "dit", "dite", "divers", "diverses", "dja", "djadjalousie", "djdjdjd", "dla", "dma", "dmes", "dmoi", "doc", "docu", "document", "documents", "dodo", "doigt", "dois", "doit", "doivent", "dollars", "domage", "domestiqu\u00e9s", "domicile", "dommage", "donc", "donjons", "donne", "donnent", "donner", "donnons", "donn\u00e9", "donn\u00e9e", "donn\u00e9es", "dont", "doodle", "dor", "doria", "dormant", "dorme", "dormir", "dors", "dos", "dose", "dot\u00e9", "double", "douce", "doucement", "douceur", "douche", "douille", "douilles", "douleur", "douleurs", "doute", "doux", "down", "dr", "drapeau", "dress\u00e9", "drogu\u00e9", "droit", "droite", "droits", "drole", "dr\u00f4le", "ds", "dsl", "du", "du_coup", "ducoup", "dur", "durant", "dur\u00e9e", "dus", "d\u00e8s", "d\u00e9barasser", "d\u00e9barrasser", "d\u00e9bloquer", "d\u00e9bourrage", "d\u00e9bourr\u00e9e", "d\u00e9brancher", "d\u00e9but", 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"http://french.irib.ir/info/item/293540-le-cinqui%C3%A8me-pouvoir-un-film-malhonn%C3%AAte-sur-wikileaks-et-julian-assange?utm_source=twitterfeed&utm_medium=twitter", "http://geekclick.org/teen-arrested-for-killing-brother-over-flappy-bird-game/", "http://huff.to/1euAs5S", "http://instagram.com/p/hCQLDyva6e/", "http://instagram.com/p/j2JJwFslxM/", "http://is.gd/PGmIE0", "http://lien.com/lien", "http://msft.it/msceo", "http://ow.ly/rMHU0", "http://ow.ly/s8G8D", "http://ow.ly/sKg9l", "http://ow.ly/tgxvy", "http://pluzz.francetv.fr/videos/les_carnets_de_julie_", "http://sdrv.ms/YADwBo", "http://twitcam.livestream.com/8yz4e", "http://vimeo.com/83523133", "http://wikileaks.org/IMG/html/gibney-transcript.html", "http://wp.me/p2we9m-1KE", "http://wp.me/p2y1rF-qo", "http://www.allocine.fr/film/fichefilm_gen_cfilm=192566.html", "http://www.cinemay.com/ser/45665/", "http://www.e-cancer.fr/le-plan-cancer", "http://www.filmosaure.com/2013/12/27/la-vie-revee-de-walter-mitty-2014/", "http://www.huffingtonpost.fr/2014/02/04/flappy-bird-jeu-fou-astuces-hack_n_4721756.html", "http://www.jeanmarcmorandini.com/article-313950-audiences-prime-tf1-leader-hier-soir-avec-le-film-hollywoo.html", "http://www.lien.com/lien", "http://www.numerama.com/magazine/28267-satya-nadella-nomme-pdg-de-microsoft.html", "http://www.pipole.net/julie-gayet-francois-hollande-bad-buzz-vsd/178592/", "http://www.starwars-holonet.com/encyclopedie/organisation-esc-rogue.html", "http://www.starwars-holonet.com/encyclopedie/planete-hoth.html", "http://www.youtube.com/watch?v=Sz9wLFises8", "http://youtu.be/GiBfYb6y8cc", "https://support.steampowered.com", "hui", "hulk", "hum", "humble", "humeur", "humm", "hummm", "humour", "hunger", "hurler", "hyin", "hype", "hypoth\u00e8se", "hypster", "h\u00e2te", "h\u00e9h\u00e9", "h\u00e9mato", "h\u00e9mogramme", "h\u00e9patique", "h\u00e9site", "h\u00e9siter", "i", "iPhone", "ia", "ibrahimovic", "ici", "identitaire", "idiots", "idole", "idoles", "id\u00e9e", 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"scandale", "scanner", "scene", "scolaire", "scooter", "score", "screen", "sc\u00e8nes", "sc\u00e9nario", "se", "seance", "sec", "second", "secondaires", "seconde", "secondes", "secouer", "secrets", "secr\u00e8tement", "section", "see", "seins", "selfie", "selle", "selon", "semaine", "semaines", "semble", "sens", "sensibilit\u00e9", "sensible", "sensibles", "sensualit\u00e9", "sent", "septembre", "sera", "serai", "seraient", "serais", "serait", "serein", "serez", "serie", "serieu", "serieuse", "serieux", "seriez", "seront", "sert", "servent", "serveur", "serveurs", "servi", "service", "ses", "seul", "seule", "seulement", "seules", "sevr\u00e9", "sexe", "sexuel", "shameless", "shirt", "shnouf", "shoot", "show-bizz", "showbiz", "shzbjfnzpb", "si", "sieste", "signifie", "similaire", "simple", "simplement", "simples", "sincler", "single", "sinon", "sirop", "sisi", "sister", "site", "situation", "situations", "situe", "si\u00e8cle", "ski", "sky", "slave", "slim", "slip", "smaine", "smicard", "smilers", "smok*", "smoquer", "sms", "snap", "so", "sochaux", "sociale", "socialistes", "soeur", "soient", "soign\u00e9e", "soin", "soins", "soir", "soir\u00e9e", "sois", "soit", "soleil", "solf\u00e8ge", "solide", "sombres", "sommeil", "sommes", "son", "sonne", "sonnerie", "sont", "sony", "sooeur", "soon", "sors", "sort", "sorte", "sortez", "sorti", "sortie", "sortir", "sortis", "soucis", "souffrance", "souffrances", "souffrez", "souffrir", "soufre", "souhaite", "souhaiter", "souhaiterez", "souhaitez", "soulager", "soul\u00e9", "souple", "source", "souriante", "sous", "sous-th\u00e8me", "soustraire", "soutien", "soutien-gorge", "soutiens", "soutient", "souven", "souvenir", "souvent", "souvents", "souvient", "so\u00fbler", "sp", "spa", "sparadrap", "spectacle", "speed", "split-screen", "spoil", "spoiler", "spoil\u00e9e", "sprenaient", "sp\u00e9cialiste", "sp\u00e9cialistes", "sp\u00e9cialit\u00e9", "sp\u00e9cifier", "sp\u00e9cifi\u00e9", "sque", "squel", "sressembl", "srfc", "srx", "st", "stabilis\u00e9", "stade", "stage", "stai", "star", "stare", "station", "stats", "steam", "steam-support", "sterilet", "stiles", "stop", "stopper", "stout", "stp", "strass", "street", "stress", "stresse", "strong", "struck", "structure", "studio", "style", "styles", "su", "subbi", "subi", "subir", "subis", "subli", "substances", "sucide", "sud", "suffisamment", "suffit", "suicide", "suis", "suisse", "suite", "suivantes", "suivent", "suivi", "sukser", "super", "superbe", "superbement", "support", "supportaire", "supporter", "suppression", "supprimer", "supprim\u00e9", "sup\u00e9rieur", "sur", "surement", "surface", "surkiff", "surpris", "surprise", "surtout", "sushis", "suuport\u00e9", "su\u00e8de", "svp", "svp\u261d\ufe0f", "svt", "swaaaaag", "swaag", "swagance", "swagg", "symbole", "sympatico", "symptomes", "sympt\u00f4mes", "symtomes", "synth\u00e8se", "syst\u00e9matique", "s\u00e8che", "s\u00e9cher", "s\u00e9curit\u00e9", "s\u00e9duit", "s\u00e9jour", 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"t\u00e9l\u00e9phone", "t\u00eate", "t\u00f4t", "u", "udithfromparis", "ue", "uhm.\u2764\ufe0f", "ultime", "ultras", "un", "un_petit_peu", "un_peu", "un_peu_plus", "une", "une_fois", "une_sorte_de", "unfollow", "union", "up", "updated", "urinaire", "urinaires", "usa", "usage", "user", "usure", "utile", "utiles", "utilisais", "utilisation", "utilise", "utilisent", "utiliser", "utilisez", "utilis\u00e9", "utilis\u00e9e", "uv", "va", "vaa", "vacances", "vacs", "vaginal", "vai", "vain", "vainqueur", "vais", "valable", "valeurs", "valide", "valise", "vampires", "vanes", "vanessa", "varient", "vas", "vase", "vaut", "vautre", "vazi", "ve", "veau", "velout\u00e9", "venaient", "venais", "venait", "vend", "vendre", "vendredi", "vends", "vener", "venere", "venir", "vent", "venu", "venue", "verbales", "verra", "verre", "verres", "vers", "vert", "vertiges", "veste", "vestiaires", "vetements", "veu", "veuille", "veuilles", "veuillez", "veulent", "veut", "veux", "vi", "via", "viande", "vibes", 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"\ud83d\ude37\ud83d\ude16", "\ud83d\ude49"], "word_dropout": 0.5}

model/weights.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd54698a9ca6d73b704911837d01cd03d1dd324470b75e5077fb9a0f98c64f62
+size 1714070316

train.log

@@ -0,0 +1,109 @@
+[hops] 2024-09-24 14:34:15.377 | INFO     | Initializing a parser from /workspace/configs/exp_camembertv2/camembertav2_base_p2_17k_last_layer.yaml
+[hops] 2024-09-24 14:34:15.407 | INFO     | Generating a FastText model from the treebank
+[hops] 2024-09-24 14:34:15.414 | INFO     | Training fasttext model
+[hops] 2024-09-24 14:34:22.479 | INFO     | Start training on cuda:1
+[hops] 2024-09-24 14:34:22.483 | WARNING  | You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+[hops] 2024-09-24 14:34:38.000 | INFO     | Epoch 0: train loss 3.1853	dev loss 2.6151	dev tag acc 15.14%	dev head acc 29.90%	dev deprel acc 43.21%
+[hops] 2024-09-24 14:34:38.002 | INFO     | New best model: head accuracy 29.90% > 0.00%
+[hops] 2024-09-24 14:34:55.908 | INFO     | Epoch 1: train loss 2.2397	dev loss 1.8289	dev tag acc 29.62%	dev head acc 50.56%	dev deprel acc 69.99%
+[hops] 2024-09-24 14:34:55.909 | INFO     | New best model: head accuracy 50.56% > 29.90%
+[hops] 2024-09-24 14:35:13.698 | INFO     | Epoch 2: train loss 1.7309	dev loss 1.4351	dev tag acc 50.91%	dev head acc 60.57%	dev deprel acc 77.66%
+[hops] 2024-09-24 14:35:13.699 | INFO     | New best model: head accuracy 60.57% > 50.56%
+[hops] 2024-09-24 14:35:31.267 | INFO     | Epoch 3: train loss 1.3681	dev loss 1.1524	dev tag acc 69.08%	dev head acc 66.75%	dev deprel acc 82.88%
+[hops] 2024-09-24 14:35:31.268 | INFO     | New best model: head accuracy 66.75% > 60.57%
+[hops] 2024-09-24 14:35:47.681 | INFO     | Epoch 4: train loss 1.1018	dev loss 0.9730	dev tag acc 74.09%	dev head acc 72.10%	dev deprel acc 83.64%
+[hops] 2024-09-24 14:35:47.682 | INFO     | New best model: head accuracy 72.10% > 66.75%
+[hops] 2024-09-24 14:36:05.527 | INFO     | Epoch 5: train loss 0.9098	dev loss 0.8205	dev tag acc 78.00%	dev head acc 75.25%	dev deprel acc 85.24%
+[hops] 2024-09-24 14:36:05.528 | INFO     | New best model: head accuracy 75.25% > 72.10%
+[hops] 2024-09-24 14:36:22.354 | INFO     | Epoch 6: train loss 0.7515	dev loss 0.7259	dev tag acc 83.95%	dev head acc 77.12%	dev deprel acc 86.37%
+[hops] 2024-09-24 14:36:22.355 | INFO     | New best model: head accuracy 77.12% > 75.25%
+[hops] 2024-09-24 14:36:40.825 | INFO     | Epoch 7: train loss 0.6351	dev loss 0.6541	dev tag acc 87.20%	dev head acc 79.35%	dev deprel acc 86.70%
+[hops] 2024-09-24 14:36:40.825 | INFO     | New best model: head accuracy 79.35% > 77.12%
+[hops] 2024-09-24 14:36:58.663 | INFO     | Epoch 8: train loss 0.5378	dev loss 0.6082	dev tag acc 89.21%	dev head acc 81.08%	dev deprel acc 87.17%
+[hops] 2024-09-24 14:36:58.664 | INFO     | New best model: head accuracy 81.08% > 79.35%
+[hops] 2024-09-24 14:37:16.472 | INFO     | Epoch 9: train loss 0.4691	dev loss 0.5612	dev tag acc 91.07%	dev head acc 80.99%	dev deprel acc 88.50%
+[hops] 2024-09-24 14:37:31.564 | INFO     | Epoch 10: train loss 0.4153	dev loss 0.5578	dev tag acc 92.18%	dev head acc 82.23%	dev deprel acc 88.90%
+[hops] 2024-09-24 14:37:31.565 | INFO     | New best model: head accuracy 82.23% > 81.08%
+[hops] 2024-09-24 14:37:48.571 | INFO     | Epoch 11: train loss 0.3739	dev loss 0.5293	dev tag acc 92.55%	dev head acc 83.24%	dev deprel acc 89.12%
+[hops] 2024-09-24 14:37:48.572 | INFO     | New best model: head accuracy 83.24% > 82.23%
+[hops] 2024-09-24 14:38:06.670 | INFO     | Epoch 12: train loss 0.3366	dev loss 0.5339	dev tag acc 93.24%	dev head acc 83.53%	dev deprel acc 89.48%
+[hops] 2024-09-24 14:38:06.671 | INFO     | New best model: head accuracy 83.53% > 83.24%
+[hops] 2024-09-24 14:38:24.365 | INFO     | Epoch 13: train loss 0.3117	dev loss 0.5259	dev tag acc 93.54%	dev head acc 83.93%	dev deprel acc 89.86%
+[hops] 2024-09-24 14:38:24.366 | INFO     | New best model: head accuracy 83.93% > 83.53%
+[hops] 2024-09-24 14:38:41.914 | INFO     | Epoch 14: train loss 0.2863	dev loss 0.5402	dev tag acc 93.51%	dev head acc 83.92%	dev deprel acc 89.87%
+[hops] 2024-09-24 14:38:57.081 | INFO     | Epoch 15: train loss 0.2669	dev loss 0.5419	dev tag acc 93.89%	dev head acc 84.29%	dev deprel acc 89.89%
+[hops] 2024-09-24 14:38:57.082 | INFO     | New best model: head accuracy 84.29% > 83.93%
+[hops] 2024-09-24 14:39:14.548 | INFO     | Epoch 16: train loss 0.2440	dev loss 0.5344	dev tag acc 94.02%	dev head acc 84.68%	dev deprel acc 90.56%
+[hops] 2024-09-24 14:39:14.549 | INFO     | New best model: head accuracy 84.68% > 84.29%
+[hops] 2024-09-24 14:39:32.313 | INFO     | Epoch 17: train loss 0.2342	dev loss 0.5615	dev tag acc 93.97%	dev head acc 84.92%	dev deprel acc 90.37%
+[hops] 2024-09-24 14:39:32.314 | INFO     | New best model: head accuracy 84.92% > 84.68%
+[hops] 2024-09-24 14:39:49.332 | INFO     | Epoch 18: train loss 0.2163	dev loss 0.5847	dev tag acc 94.21%	dev head acc 84.47%	dev deprel acc 90.54%
+[hops] 2024-09-24 14:40:04.450 | INFO     | Epoch 19: train loss 0.2017	dev loss 0.5894	dev tag acc 94.22%	dev head acc 85.00%	dev deprel acc 90.76%
+[hops] 2024-09-24 14:40:04.451 | INFO     | New best model: head accuracy 85.00% > 84.92%
+[hops] 2024-09-24 14:40:22.784 | INFO     | Epoch 20: train loss 0.1901	dev loss 0.5888	dev tag acc 94.34%	dev head acc 85.31%	dev deprel acc 90.99%
+[hops] 2024-09-24 14:40:22.784 | INFO     | New best model: head accuracy 85.31% > 85.00%
+[hops] 2024-09-24 14:40:40.285 | INFO     | Epoch 21: train loss 0.1786	dev loss 0.6046	dev tag acc 94.35%	dev head acc 85.54%	dev deprel acc 90.91%
+[hops] 2024-09-24 14:40:40.286 | INFO     | New best model: head accuracy 85.54% > 85.31%
+[hops] 2024-09-24 14:40:58.101 | INFO     | Epoch 22: train loss 0.1680	dev loss 0.6209	dev tag acc 94.51%	dev head acc 85.56%	dev deprel acc 90.92%
+[hops] 2024-09-24 14:40:58.102 | INFO     | New best model: head accuracy 85.56% > 85.54%
+[hops] 2024-09-24 14:41:16.021 | INFO     | Epoch 23: train loss 0.1592	dev loss 0.6549	dev tag acc 94.57%	dev head acc 85.32%	dev deprel acc 91.15%
+[hops] 2024-09-24 14:41:31.264 | INFO     | Epoch 24: train loss 0.1496	dev loss 0.6540	dev tag acc 94.63%	dev head acc 85.78%	dev deprel acc 91.18%
+[hops] 2024-09-24 14:41:31.265 | INFO     | New best model: head accuracy 85.78% > 85.56%
+[hops] 2024-09-24 14:41:48.616 | INFO     | Epoch 25: train loss 0.1423	dev loss 0.6558	dev tag acc 94.63%	dev head acc 85.49%	dev deprel acc 91.17%
+[hops] 2024-09-24 14:42:04.233 | INFO     | Epoch 26: train loss 0.1339	dev loss 0.6730	dev tag acc 94.75%	dev head acc 86.01%	dev deprel acc 91.23%
+[hops] 2024-09-24 14:42:04.234 | INFO     | New best model: head accuracy 86.01% > 85.78%
+[hops] 2024-09-24 14:42:22.418 | INFO     | Epoch 27: train loss 0.1301	dev loss 0.7089	dev tag acc 94.78%	dev head acc 85.80%	dev deprel acc 91.28%
+[hops] 2024-09-24 14:42:37.688 | INFO     | Epoch 28: train loss 0.1210	dev loss 0.7102	dev tag acc 94.85%	dev head acc 86.12%	dev deprel acc 91.34%
+[hops] 2024-09-24 14:42:37.689 | INFO     | New best model: head accuracy 86.12% > 86.01%
+[hops] 2024-09-24 14:42:54.569 | INFO     | Epoch 29: train loss 0.1140	dev loss 0.7184	dev tag acc 94.85%	dev head acc 85.87%	dev deprel acc 91.29%
+[hops] 2024-09-24 14:43:10.080 | INFO     | Epoch 30: train loss 0.1121	dev loss 0.7474	dev tag acc 94.74%	dev head acc 85.97%	dev deprel acc 91.67%
+[hops] 2024-09-24 14:43:25.793 | INFO     | Epoch 31: train loss 0.1082	dev loss 0.7457	dev tag acc 94.88%	dev head acc 85.99%	dev deprel acc 91.58%
+[hops] 2024-09-24 14:43:40.730 | INFO     | Epoch 32: train loss 0.1038	dev loss 0.7704	dev tag acc 94.81%	dev head acc 86.14%	dev deprel acc 91.37%
+[hops] 2024-09-24 14:43:40.731 | INFO     | New best model: head accuracy 86.14% > 86.12%
+[hops] 2024-09-24 14:43:58.212 | INFO     | Epoch 33: train loss 0.0975	dev loss 0.7758	dev tag acc 94.87%	dev head acc 86.52%	dev deprel acc 91.39%
+[hops] 2024-09-24 14:43:58.213 | INFO     | New best model: head accuracy 86.52% > 86.14%
+[hops] 2024-09-24 14:44:16.413 | INFO     | Epoch 34: train loss 0.0951	dev loss 0.7914	dev tag acc 94.84%	dev head acc 86.33%	dev deprel acc 91.41%
+[hops] 2024-09-24 14:44:31.475 | INFO     | Epoch 35: train loss 0.0904	dev loss 0.8350	dev tag acc 94.91%	dev head acc 86.53%	dev deprel acc 91.53%
+[hops] 2024-09-24 14:44:31.476 | INFO     | New best model: head accuracy 86.53% > 86.52%
+[hops] 2024-09-24 14:44:48.600 | INFO     | Epoch 36: train loss 0.0855	dev loss 0.8383	dev tag acc 94.94%	dev head acc 86.34%	dev deprel acc 91.45%
+[hops] 2024-09-24 14:45:04.629 | INFO     | Epoch 37: train loss 0.0852	dev loss 0.8443	dev tag acc 94.91%	dev head acc 86.42%	dev deprel acc 91.62%
+[hops] 2024-09-24 14:45:19.577 | INFO     | Epoch 38: train loss 0.0821	dev loss 0.8490	dev tag acc 94.98%	dev head acc 86.45%	dev deprel acc 91.47%
+[hops] 2024-09-24 14:45:34.440 | INFO     | Epoch 39: train loss 0.0788	dev loss 0.8424	dev tag acc 94.92%	dev head acc 86.29%	dev deprel acc 91.43%
+[hops] 2024-09-24 14:45:48.748 | INFO     | Epoch 40: train loss 0.0768	dev loss 0.8570	dev tag acc 95.08%	dev head acc 86.55%	dev deprel acc 91.52%
+[hops] 2024-09-24 14:45:48.750 | INFO     | New best model: head accuracy 86.55% > 86.53%
+[hops] 2024-09-24 14:46:06.299 | INFO     | Epoch 41: train loss 0.0740	dev loss 0.8655	dev tag acc 95.11%	dev head acc 86.34%	dev deprel acc 91.52%
+[hops] 2024-09-24 14:46:22.078 | INFO     | Epoch 42: train loss 0.0709	dev loss 0.8882	dev tag acc 95.04%	dev head acc 86.49%	dev deprel acc 91.47%
+[hops] 2024-09-24 14:46:37.401 | INFO     | Epoch 43: train loss 0.0685	dev loss 0.8956	dev tag acc 95.09%	dev head acc 86.32%	dev deprel acc 91.63%
+[hops] 2024-09-24 14:46:52.792 | INFO     | Epoch 44: train loss 0.0673	dev loss 0.9303	dev tag acc 95.06%	dev head acc 86.42%	dev deprel acc 91.53%
+[hops] 2024-09-24 14:47:08.151 | INFO     | Epoch 45: train loss 0.0652	dev loss 0.9314	dev tag acc 95.10%	dev head acc 86.33%	dev deprel acc 91.47%
+[hops] 2024-09-24 14:47:23.787 | INFO     | Epoch 46: train loss 0.0623	dev loss 0.9163	dev tag acc 95.10%	dev head acc 86.43%	dev deprel acc 91.48%
+[hops] 2024-09-24 14:47:39.059 | INFO     | Epoch 47: train loss 0.0599	dev loss 0.9738	dev tag acc 95.04%	dev head acc 86.47%	dev deprel acc 91.62%
+[hops] 2024-09-24 14:47:54.002 | INFO     | Epoch 48: train loss 0.0587	dev loss 0.9811	dev tag acc 95.12%	dev head acc 86.31%	dev deprel acc 91.50%
+[hops] 2024-09-24 14:48:08.900 | INFO     | Epoch 49: train loss 0.0564	dev loss 0.9841	dev tag acc 95.02%	dev head acc 86.55%	dev deprel acc 91.57%
+[hops] 2024-09-24 14:48:08.902 | INFO     | New best model: head accuracy 86.55% > 86.55%
+[hops] 2024-09-24 14:48:26.237 | INFO     | Epoch 50: train loss 0.0543	dev loss 0.9959	dev tag acc 95.05%	dev head acc 86.51%	dev deprel acc 91.54%
+[hops] 2024-09-24 14:48:41.164 | INFO     | Epoch 51: train loss 0.0539	dev loss 0.9901	dev tag acc 95.10%	dev head acc 86.47%	dev deprel acc 91.64%
+[hops] 2024-09-24 14:48:57.119 | INFO     | Epoch 52: train loss 0.0535	dev loss 1.0037	dev tag acc 95.09%	dev head acc 86.47%	dev deprel acc 91.67%
+[hops] 2024-09-24 14:49:11.881 | INFO     | Epoch 53: train loss 0.0515	dev loss 1.0083	dev tag acc 95.17%	dev head acc 86.53%	dev deprel acc 91.63%
+[hops] 2024-09-24 14:49:27.377 | INFO     | Epoch 54: train loss 0.0495	dev loss 1.0323	dev tag acc 95.18%	dev head acc 86.59%	dev deprel acc 91.55%
+[hops] 2024-09-24 14:49:27.378 | INFO     | New best model: head accuracy 86.59% > 86.55%
+[hops] 2024-09-24 14:49:44.663 | INFO     | Epoch 55: train loss 0.0494	dev loss 1.0093	dev tag acc 95.16%	dev head acc 86.47%	dev deprel acc 91.53%
+[hops] 2024-09-24 14:49:59.239 | INFO     | Epoch 56: train loss 0.0489	dev loss 1.0157	dev tag acc 95.13%	dev head acc 86.55%	dev deprel acc 91.57%
+[hops] 2024-09-24 14:50:13.539 | INFO     | Epoch 57: train loss 0.0475	dev loss 1.0208	dev tag acc 95.20%	dev head acc 86.52%	dev deprel acc 91.57%
+[hops] 2024-09-24 14:50:28.650 | INFO     | Epoch 58: train loss 0.0465	dev loss 1.0348	dev tag acc 95.16%	dev head acc 86.50%	dev deprel acc 91.65%
+[hops] 2024-09-24 14:50:44.170 | INFO     | Epoch 59: train loss 0.0459	dev loss 1.0435	dev tag acc 95.20%	dev head acc 86.56%	dev deprel acc 91.66%
+[hops] 2024-09-24 14:50:59.437 | INFO     | Epoch 60: train loss 0.0445	dev loss 1.0493	dev tag acc 95.21%	dev head acc 86.59%	dev deprel acc 91.74%
+[hops] 2024-09-24 14:51:13.819 | INFO     | Epoch 61: train loss 0.0447	dev loss 1.0545	dev tag acc 95.21%	dev head acc 86.69%	dev deprel acc 91.68%
+[hops] 2024-09-24 14:51:13.820 | INFO     | New best model: head accuracy 86.69% > 86.59%
+[hops] 2024-09-24 14:51:31.044 | INFO     | Epoch 62: train loss 0.0437	dev loss 1.0525	dev tag acc 95.21%	dev head acc 86.64%	dev deprel acc 91.65%
+[hops] 2024-09-24 14:51:46.773 | INFO     | Epoch 63: train loss 0.0427	dev loss 1.0558	dev tag acc 95.21%	dev head acc 86.60%	dev deprel acc 91.66%
+[hops] 2024-09-24 14:51:52.241 | WARNING  | You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+[hops] 2024-09-24 14:51:59.722 | WARNING  | You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+[hops] 2024-09-24 14:52:02.962 | INFO     | Metrics for FSMB-camembertav2_base_p2_17k_last_layer+rand_seed=123
+ ─────────────────────────────── 
+  Split   UPOS     UAS     LAS   
+ ─────────────────────────────── 
+  Dev     95.12   86.90   81.38  
+  Test    95.09   86.76   81.67  
+ ─────────────────────────────── 
+