Automated hub push by babel_finetune_agent

README.md

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+---
+model-index:
+- name: poltextlab/xlm-roberta-large-ineq-binary-v6
+  results:
+  - task:
+      type: text-classification
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: N/A
+    - name: F1-Score
+      type: f1
+      value: 46%
+tags:
+- text-classification
+- pytorch
+metrics:
+- precision
+- recall
+- f1-score
+language:
+- en
+base_model:
+- xlm-roberta-large
+pipeline_tag: text-classification
+library_name: transformers
+license: cc-by-4.0
+extra_gated_prompt: Our models are intended for academic use only. If you are not
+  affiliated with an academic institution, please provide a rationale for using our
+  models. Please allow us a few business days to manually review subscriptions.
+extra_gated_fields:
+  Name: text
+  Country: country
+  Institution: text
+  Institution Email: text
+  Please specify your academic use case: text
+---
+
+# xlm-roberta-large-ineq-binary-v6
+
+
+# How to use the model
+
+```python
+from transformers import AutoTokenizer, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
+pipe = pipeline(
+    model="poltextlab/xlm-roberta-large-ineq-binary-v6",
+    task="text-classification",
+    tokenizer=tokenizer,
+    use_fast=False,
+    token="<your_hf_read_only_token>"
+)
+
+text = "<text_to_classify>"
+pipe(text)
+```
+        
+
+# Classification Report
+
+## Overall Performance:
+
+* **Accuracy:** N/A
+* **Macro Avg:** Precision: 0.70, Recall: 0.56, F1-score: 0.46
+* **Weighted Avg:** Precision: 0.70, Recall: 0.56, F1-score: 0.46
+
+## Per-Class Metrics:
+
+| Label                      |   Precision |   Recall |   F1-score |   Support |
+|:---------------------------|------------:|---------:|-----------:|----------:|
+| (0) Not inequality related |        0.53 |     0.98 |       0.69 |        51 |
+| (1) Inequality related     |        0.88 |     0.14 |       0.24 |        51 |
+
+# Inference platform
+This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.  
+
+# Cooperation
+Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com).
+## Debugging and issues
+This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually.

config.json

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+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "Not inequality related",
+    "1": "Inequality related"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "Inequality related": 1,
+    "Not inequality related": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

finetune_config.json

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+{
+  "model_path": "xlm-roberta-large",
+  "model_type": "encoder",
+  "learning_rate": 5e-07,
+  "epochs": 15,
+  "batch_size": 16,
+  "max_seq_length": 256,
+  "domain": "migration"
+}

model.safetensors

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+oid sha256:fb7a7a7bf256f0249a7afab2b25b9fbb730cf17d10824d1a812e8238d8afc328
+size 2239618672

training_args.bin

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+oid sha256:227d1389719a6b2cf66e78bc885315e480f46d5ad8ba37892e69c2ff10c77aa6
+size 5713